Antoine Yang

Arsha Nagrani

Ivan Laptev

Josef Sivic

Cordelia Schmid

VidChapters-7M: Video Chapters at Scale

Segmenting long videos into chapters enables users to quickly navigate to the information of their interest. This important topic has been understudied due to the lack of publicly released datasets. To address this issue, we present VidChapters-7M, a dataset of 817K user-chaptered videos including 7M chapters in total. VidChapters-7M is automatically created from videos online in a scalable manner by scraping user-annotated chapters and hence without any additional manual annotation. We introduce the following three tasks based on this data. First, the video chapter generation task consists of temporally segmenting the video and generating a chapter title for each segment. To further dissect the problem, we also define two variants of this task: video chapter generation given ground-truth boundaries, which requires generating a chapter title given an annotated video segment, and video chapter grounding, which requires temporally localizing a chapter given its annotated title. We benchmark both simple baselines and state-of-the-art video-language models for these three tasks. We also show that pretraining on VidChapters-7M transfers well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 and ViTT benchmarks. Finally, our experiments reveal that downstream performance scales well with the size of the pretraining dataset. Our dataset, code, and models are publicly available at https://antoyang.github.io/vidchapters.html. Intro and new shoes Hiking Low Tatras todays plan Beautifull ascent Low Tatras New shoes, la Sportiva x4 Back to the campervan

Introduction

As online media consumption grows, the volume of video content available is increasing rapidly. While searching for specific videos is already a challenging problem, searching within a long video is an even less explored task. Manual navigation can often be time consuming, particularly for long videos. A compelling solution for organizing content online is to segment long videos into chapters (see Figure 1). Chapters are contiguous, non-overlapping segments, completely partitioning a video. Each chapter is also labeled with a short description of the chapter content, enabling users to quickly navigate to areas of interest and easily replay different parts of a video. Chapters also give structure to a video, which is useful for long videos that contain inherently listed content, such as listicles [START_REF] Vijgen | The listicle: An exploring research on an interesting shareable new media phenomenon[END_REF], instructional videos [64], music compilations and so on.

Given the plethora of content already online, our goal is to explore automatic solutions related to video chaptering -generating chapters automatically, and grounding chapter titles temporally in long videos. While the benefits of automatically chaptering videos are obvious, data for this task is scarce. Video captioning datasets (such as WebVid-10M [5] and VideoCC [START_REF] Nagrani | Learning audio-video modalities from image captions[END_REF]) consist of short videos (10s in length), and hence are unsuitable. Web datasets consisting of longer videos (HowTo100M [64], YT-Temporal-1B [START_REF] Zellers | MERLOT Reserve: Neural script knowledge through vision and language and sound[END_REF]) come with aligned speech transcripts (ASR), which are only weakly related to visual content, and if used as chapter titles would tend to over-segment videos. Moment retrieval [START_REF] Gao | TALL: Temporal activity localization via language query[END_REF][START_REF] Lisa | Localizing moments in video with natural language[END_REF] or dense video captioning [START_REF] Krishna | Densecaptioning events in videos[END_REF][START_REF] Zhou | Towards automatic learning of procedures from web instructional videos[END_REF] datasets are perhaps the most useful, but do not focus on creating explicit structure, and instead describe low-level actions comprehensively. Such datasets are also manually annotated, and hence not scalable and small in size (see Table 1).

To remedy this, we curate VidChapters-7M, a large-scale dataset of user-annotated video chapters automatically scraped from the Web. Our dataset consists of 7M chapters for over 817K videos. Compared to existing datasets, videos in VidChapters-7M are long (23 minutes on average) and contain rich chapter annotations consisting of a starting timestamp and a title per chapter. Our dataset is also diverse, with 12 different video categories having at least 20K videos each, which itself is the size of existing dense video captioning datasets [START_REF] Grauman | Ego4D: Around the World in 3,000 Hours of Egocentric Video[END_REF][START_REF] Huang | Multimodal pretraining for dense video captioning[END_REF][START_REF] Krishna | Densecaptioning events in videos[END_REF][START_REF] Zhou | Towards automatic learning of procedures from web instructional videos[END_REF]. On top of this dataset we also define 3 video tasks (see Figure 2): (i) video chapter generation which requires temporally segmenting the video and generating a chapter title for each segment; (ii) video chapter generation given ground-truth boundaries , which requires generating a chapter title given an annotated video segment; and (iii) video chapter grounding , which requires temporally localizing a chapter given the chapter title. All three tasks involve parsing and understanding long videos, and multi-modal reasoning (video and text), and hence are valuable steps towards story understanding.

For all three tasks, we implement simple baselines as well as recent, state-of-the-art video-text methods [START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF][START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF][START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF]. We find that the tasks are far from being solved, demonstrating the value of this problem. Interestingly, we also show that our video chapter generation models trained on VidChapters-7M transfer well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 [START_REF] Zhou | Towards automatic learning of procedures from web instructional videos[END_REF] and ViTT benchmarks [START_REF] Huang | Multimodal pretraining for dense video captioning[END_REF]. Moreover, we show that pretraining using both speech transcripts and chapter annotations significantly outperforms the widely used pretraining method based only on speech transcripts [START_REF] Miech | End-to-end learning of visual representations from uncurated instructional videos[END_REF][START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF][START_REF] Zellers | MERLOT Reserve: Neural script knowledge through vision and language and sound[END_REF]. This demonstrates the additional value of our dataset as a generic video-language pretraining set. Interestingly, we also find that the transfer performance scales with the size of the chapter dataset.

In summary, our contributions are:

(i) We present VidChapters-7M, a large-scale dataset of user-annotated video chapters obtained from the Web consisting of 817K videos and 7M chapters;

(ii) Based on this dataset, we evaluate a range of simple baselines and state-of-the-art videolanguage models on the tasks of video chapter generation with and without ground-truth boundaries, and video chapter grounding;

(iii) We show that video chapter generation models trained on VidChapters-7M transfer well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 [START_REF] Zhou | Towards automatic learning of procedures from web instructional videos[END_REF] and ViTT benchmarks [START_REF] Huang | Multimodal pretraining for dense video captioning[END_REF], outperforming prior pretraining methods based on narrated videos [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF], and showing promising scaling behavior.

Our dataset, code and models are publicly available on our website [START_REF]VidChapters-7M project webpage[END_REF]. We consider open-sourced video datasets that contain dense natural language descriptions aligned over time. VidChapters-7M is much larger than current dense video captioning datasets. Compared to datasets with ASR (top 3 rows), it is smaller in the total number of videos but contains longer videos with richer annotations (chapters).

Related Work

Large-scale vision-language datasets. The development of powerful multi-modal models [3, 15, 23, 35, 37, 38, 46, 48-50, 54, 61, 62, 72, 85, 87, 90, 94, 99, 105, 115, 116, 129] has been made possible by pretraining on large-scale image-caption datasets scraped from the Web such as SBU [START_REF] Ordonez | Im2text: Describing images using 1 million captioned photographs[END_REF], Conceptual Captions [START_REF] Sharma | Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning[END_REF], Conceptual-12M [START_REF] Changpinyo | Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts[END_REF], LAIT [START_REF] Qi | ImageBERT: Cross-modal pre-training with large-scale weak-supervised image-text data[END_REF], Wikipedia-ImageText [START_REF] Srinivasan | Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning[END_REF], RedCaps [18] and LAION-5B [START_REF] Schuhmann | LAION-5B: An open large-scale dataset for training next generation image-text models[END_REF]. Similarly, many strong video-language models [START_REF] Akbari | VATT: Transformers for multimodal self-supervised learning from raw video, audio and text[END_REF][START_REF] Ge | Bridging video-text retrieval with multiple choice questions[END_REF][START_REF] Han | Temporal alignment networks for long-term video[END_REF][START_REF] Ko | Video-text representation learning via differentiable weak temporal alignment[END_REF][START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF][START_REF] Banerjee | METEOR: An automatic metric for mt evaluation with improved correlation with human judgments[END_REF][START_REF] Li | HERO: Hierarchical encoder for video+language omni-representation pre-training[END_REF]53,[START_REF] Qinghong | Egocentric videolanguage pretraining[END_REF][START_REF] Miech | End-to-end learning of visual representations from uncurated instructional videos[END_REF][START_REF] Hongsuck | Look before you speak: Visually contextualized utterances[END_REF][START_REF] Hongsuck | End-to-end generative pretraining for multimodal video captioning[END_REF]88,[START_REF] Sun | Long-form video-language pre-training with multimodal temporal contrastive learning[END_REF][START_REF] Tang | TVLT: Textless vision-language transformer[END_REF]97,[START_REF] Wang | Object-aware video-language pre-training for retrieval[END_REF][START_REF] Xu | VideoCLIP: Contrastive pre-training for zero-shot video-text understanding[END_REF][START_REF] Yang | Just ask: Learning to answer questions from millions of narrated videos[END_REF][START_REF] Yang | Learning to answer visual questions from web videos[END_REF][START_REF] Yang | Zero-shot video question answering via frozen bidirectional language models[END_REF][START_REF] Zhao | Learning video representations from large language models[END_REF] have been pretrained on Web-scraped video-text datasets. These datasets are largely composed of short videos paired with captions, e.g. WebVid-10M [5] and VideoCC [START_REF] Nagrani | Learning audio-video modalities from image captions[END_REF], or narrated videos with speech transcripts aligned over time (ASR), e.g. HowTo100M [64], YT-Temporal-1B [START_REF] Zellers | MERLOT: Multimodal neural script knowledge models[END_REF][START_REF] Zellers | MERLOT Reserve: Neural script knowledge through vision and language and sound[END_REF] and HD-VILA-100M [START_REF] Hongwei Xue | Advancing high-resolution video-language representation with large-scale video transcriptions[END_REF]. Our proposed VidChapters-7M dataset is also downloaded from the Web, via a scalable pipeline without the need for expensive manual annotation. Unlike these datasets, VidChapters-7M consists of long videos with user-annotated chapters aligned over time (see Table 1), which significantly differ from ASR (see Section 3.3). Furthermore, most videos in VidChapters-7M also contain ASR. Finally, VidChapters-7M is also related to the recent ChapterGen dataset [START_REF] Cao | Multi-modal video chapter generation[END_REF], which also consists of user-annotated chapters. However, ChapterGen is several orders of magnitude smaller than VidChapters-7M (10K vs 817K videos) and is not open-sourced at the time of writing.

Video tasks. The video chapter generation task requires temporally segmenting the video into chapters, hence is related to video shot detection [START_REF] Rasheed | Scene detection in hollywood movies and tv shows[END_REF][START_REF] Rui | Exploring video structure beyond the shots[END_REF][START_REF] Sidiropoulos | Temporal video segmentation to scenes using high-level audiovisual features[END_REF], movie scene segmentation [START_REF] Chen | Shot contrastive self-supervised learning for scene boundary detection[END_REF][START_REF] Rao | A local-to-global approach to multi-modal movie scene segmentation[END_REF], temporal action localization [START_REF] Chao | Rethinking the Faster R-CNN architecture for temporal action localization[END_REF]16,[START_REF] Liu | Endto-end temporal action detection with transformer[END_REF]83,[START_REF] Zeng | Graph convolutional networks for temporal action localization[END_REF][START_REF] Zhang | ActionFormer: Localizing moments of actions with transformers[END_REF] and temporal action segmentation [START_REF] Behrmann | Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation[END_REF][START_REF] Abu | MS-TCN: Multi-stage temporal convolutional network for action segmentation[END_REF][START_REF] Gao | Global2Local: Efficient structure search for video action segmentation[END_REF][START_REF] Lea | Temporal convolutional networks for action segmentation and detection[END_REF]55,[START_REF] Wang | Boundary-aware cascade networks for temporal action segmentation[END_REF]. However, unlike these tasks, video chapter generation also requires generating a free-form natural language chapter title for each segment. Hence this task is also related to video captioning [START_REF] Gao | Video captioning with attention-based lstm and semantic consistency[END_REF][START_REF] Lin | SwinBERT: End-to-end transformers with sparse attention for video captioning[END_REF][START_REF] Luo | UniViLM: A unified video and language pre-training model for multimodal understanding and generation[END_REF][START_REF] Pan | Video captioning with transferred semantic attributes[END_REF][START_REF] Wang | Reconstruction network for video captioning[END_REF][START_REF] Wang | Video captioning via hierarchical reinforcement learning[END_REF][START_REF] Zhang | Object relational graph with teacher-recommended learning for video captioning[END_REF], video title generation [4, [START_REF] Zeng | Title generation for user generated videos[END_REF][START_REF] Zhang | Comprehensive information integration modeling framework for video titling[END_REF], generic event boundary captioning [START_REF] Wang | GEB+: A benchmark for generic event boundary captioning, grounding and retrieval[END_REF] and dense video captioning [START_REF] Krishna | Densecaptioning events in videos[END_REF][START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF][START_REF] Zhou | End-to-end dense video captioning with masked transformer[END_REF]. Most related to video chapter generation, the dense video captioning task requires temporally localizing and captioning all events in an untrimmed video. In contrast, video chapter generation requires temporally segmenting the video (i.e. the start of the chapter i + 1 is the end of chapter i, and the chapters cover the full video), and involves generating a chapter title that is substantially shorter than a video caption. We study in more detail the transfer learning between these two tasks in Section 4.4. Finally, the video chapter grounding task is related to temporal language grounding [START_REF] Lisa | Localizing moments in video with natural language[END_REF]34,[START_REF] Lei | TVR: A large-scale dataset for video-subtitle moment retrieval[END_REF][START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF][START_REF] Nan | Interventional video grounding with dual contrastive learning[END_REF][START_REF] Yang | TubeDETR: Spatio-temporal video grounding with transformers[END_REF][START_REF] Zhang | Span-based localizing network for natural language video localization[END_REF][START_REF] Zhang | Learning 2d temporal adjacent networks for moment localization with natural language[END_REF]. However, we here focus on localizing a chapter starting point and not a start-end window. Furthermore, most temporal language grounding methods represent the video only with visual inputs, while we also exhibit the benefits of using speech inputs for localizing chapters in videos (see Section 4.3).

VidChapters-7M: a large-scale dataset of user-chaptered videos

Our goal is to build a large and diverse set of videos annotated with temporarily localized chapter information, consisting of chapter titles and chapter start times. In detail, chapters are contiguous, non-overlapping segments, completely partitioning a video. However manual annotation of chapters is time consuming and expensive and therefore hard to scale. Hence we automatically scrape chapter information from videos available online, as explained in Section 3.1. Then, we perform several processing steps on this data, e.g., to extract speech transcripts, as described in Section 3.2. The outcome is VidChapters-7M, a dataset of 817K videos with 7M chapter annotations provided by real users online. Finally, we analyze VidChapters-7M in Section 3.3. Details are given next.

Data collection

Since early 2020, YouTube users can create chapters for uploaded videos by annotating them in the YouTube description. The YouTube API, however, currently does not enable explicit search for user-chaptered videos. Hence, our data collection procedure consists of: (i) Collecting a large and diverse set of video candidates (characterized by their 11-character YouTube video ID), which do not necessarily contain user-annotated chapters; (ii) For all video candidates, downloading the video description, automatically selecting videos with user-annotated chapters, extracting video chapters and downloading corresponding videos. We next describe the individual steps in more detail.

Video candidates. We start from a large pool of video candidates built from the YT-Temporal-180M dataset [START_REF] Zellers | MERLOT: Multimodal neural script knowledge models[END_REF], which was constructed to be more diverse than prior large video datasets such as HowTo100M [64]. Note that while the released YT-Temporal-180M dataset consists of only 5M videos, the authors collected a larger set of candidates by using YouTube's recommendation algorithm to suggest related videos. We obtained this extended list of 92 million video IDs directly from the authors.

Extracting chapters from descriptions. In the description, chapters typically constitute a block with consecutive lines following the format "<Timestamp>: <Chapter Title>" or "<Chapter Title>: <Timestamp>", where the chapter title is written in free-form natural language and its corresponding start timestamp is written in MM:SS format. The video should contain at least two timestamps listed in ascending order. Hence we download the descriptions for all video candidates and use standard regular expression operations to verify whether a given description contains user-annotated chapters and extract them if so. Note that some videos contain chapters that are automatically generated by YouTube algorithms, however, these generated chapters do not appear in the descriptions and, hence, are excluded by our procedure for data collection. Also note that the video content is only downloaded for user-chaptered videos, which is convenient for both the downloading speed and storage constraints. Finally, we obtain 817K user-chaptered videos, making up 0.9% of all video candidates.

Data processing

We describe below how we process the previously obtained user-chaptered videos to facilitate building efficient video chapter generation models. For reproducibility, we publicly release the resulting speech transcripts and the code for extracting visual features. ASR extraction. We observed that most user-chaptered videos contain speech. Hence, for all videos, we extract speech transcripts aligned in time with the video content (ASR) by applying the Whisper-Large-V2 model [START_REF] Radford | Robust speech recognition via large-scale weak supervision[END_REF] on the audio track, using faster-whisper [START_REF] Klein | faster-whisper library[END_REF] backend for computational efficiency. We found that the Whisper model provides higher-quality ASR compared to the YouTube API ASR service on several data samples from VidChapters-7M. We further use WhisperX [START_REF] Bain | WhisperX: Time-accurate speech transcription of long-form audio[END_REF] to derive accurate word-level timestamps which we use to segment the speech transcript into sentences. For example, the Whisper-Large-V2 model extracts speech segments like "Right, we're gonna do the Synthetics Dirty Race. No we're not. [...] So we're gonna put two t-shirts and two pairs of jeans in the" with timestamps 20.478s and 50.465s, and the corresponding first sentence output by WhisperX is "Right, we're gonna do the Synthetics Dirty Race." with timestamps 20.538s and 29.26s.

Visual feature extraction.

Training end-to-end deep learning models from RGB inputs on minuteslong videos is computationally expensive. Hence we extract visual features with CLIP ViT-L/14 backbone [START_REF] Dosovitskiy | An image is worth 16x16 words: Transformers for image recognition at scale[END_REF][START_REF] Radford | Learning transferable visual models from natural language supervision[END_REF] at resolution 224 × 224 pixels and 1 FPS. This model has been trained to map images to text descriptions with a contrastive loss on 400M Web-scraped image-text pairs.

Data analysis

The result of the previously described pipeline is VidChapters-7M, a dataset of 817,076 user-chaptered videos containing 6,813,732 chapters in total. We randomly split VidChapters-7M in training, validation, and testing splits with 801K, 8.2K, and 8.2K videos, respectively. We analyze VidChapters- ASR vs Chapters. 97.3% of videos in VidChapters-7M contain speech transcripts (ASR). However, user-annotated chapters significantly differ from speech transcripts: on average, a video with ASR contains 269.8 speech sentences (vs 8.3 chapter titles), a speech sentence lasts 3.9 seconds (vs 142.0 seconds for chapters) in the video and contains 11.5 words (vs 5.4 words for chapters).

Biases. Using the langdetect [START_REF] Danilák | Language detection library[END_REF] language detection tool, we find that 92.9%/93.9% of total videos in VidChapters-7M have their chapter titles/ASR in English. However, as shown in Figure 3 (bottom right), the distribution of chapter languages includes a long tail of languages, e.g., 13 languages appear in more than 1K videos of VidChapters-7M. We also use GenBit [START_REF] Sengupta | Genbit: measure and mitigate gender bias in language datasets[END_REF] to measure gender bias in the chapters and ASR. We observe that the percentage of female/male/non-binary gendered words is 19.7%/39.7%/40.7% for the chapters, and 11.6%/35.6%/52.8% for the ASR.

Ethical considerations. We employ several techniques to identify harmful visual or language content. We use a classifier [START_REF] Schuhmann | LAION-5B: An open large-scale dataset for training next generation image-text models[END_REF] built on top of the previously extracted CLIP features to detect not-safe-forwork (NSFW) visual content (such as pornographic and sexualized content). Moreover, we use a language model [START_REF] Hanu | Unitary team[END_REF] to detect toxic content in chapter titles and speech transcripts. These processes flag 5,716 (0.70%) visually NSFW videos, 355 (0.04%) videos with toxic chapter titles and 1,368 (0.17%) videos with toxic ASR. We assume the relatively low number of flagged videos is due to the regulations performed by the Web platform used to collect our dataset. Following [START_REF] Schuhmann | LAION-5B: An open large-scale dataset for training next generation image-text models[END_REF], we refrain from removing these samples to encourage research in fields such as dataset curation and tag them instead. Note that these automated filtering techniques are not perfect and that harmful content may pass. Table 2: Manual assessment of the informativeness of chapter titles in the VidChapters-7M dataset over a random sample of 100 videos. Video chapter titles can be based on speech and vision; audio and vision; vision, audio or speech alone; or only on the structure of the video (e.g. "step 1", "step 2" etc). In a small number of cases, video chapters are unrelated to the video content.

Manual assessment of the quality of annotations. While chapter titles are manually written and uploaded by real users, sometimes chapter titles are not informative about the content of the video at the corresponding timestamps. To assess the quality of chapter title annotations in our dataset, we inspected a random sample of 100 videos in VidChapters-7M. For each video, we checked if the titles are related to the content of the video chapter and if so which video modalities (ASR, visual or raw audio) they are related to, or if they only refer to the structure of the video (e.g. chapter titles like "step 1", "step 2" etc). Results are presented in Table 2, and show that 83% of videos have chapters related to one or multiple modalities of the video, 14% of videos have chapters only referring to the structure of the video, and 3% of videos have chapters unrelated to the video content.

Experiments

In this Section, we present the results of models on VidChapters-7M for the full video chapter generation task in Section 4.1, the task of video chapter generation given ground-truth boundaries in Section 4.2 and the video chapter grounding task in Section 4.3. Finally, we study transfer learning from video chapter generation to dense video captioning tasks in Section 4.4.

Evaluation metrics. To evaluate the quality of the generated chapter titles (without their positions), we use standard metrics used for visual captioning: BLEU [START_REF] Papineni | BLEU: a method for automatic evaluation of machine translation[END_REF] (B), CIDEr [START_REF] Vedantam | CIDEr: Consensus-based image description evaluation[END_REF] (C), METEOR [7] (M) and ROUGE-L [START_REF] Lin | Rouge: a package for automatic evaluation of summaries[END_REF] (RL). To evaluate video chapter generation as a whole, including the locations of the generated chapters, we follow standard protocols used for dense video captioning, given the similar nature of the two tasks. We use the standard evaluation tool [START_REF] Krishna | Densecaptioning events in videos[END_REF] which calculates matched pairs between generated events and the ground truth across IoU thresholds of {0.3, 0.5, 0.7, 0.9}, and compute captioning metrics over the matched pairs. However, these metrics do not take into account the story of the video and give high scores to methods generating many redundant chapters. Hence for an overall evaluation, we also use SODA_c [START_REF] Fujita | SODA: Story oriented dense video captioning evaluation framework[END_REF] (S) which first tries to find a temporally optimal matching between generated and reference chapters to capture the story of a video, then computes METEOR scores for the matching and derives F-measure scores from the METEOR scores to penalize redundant chapters. To separately evaluate chapter localization, we report the recall (R@Ks, R@K) and the precision (P@Ks, P@K) across various thresholds in terms of the distance to the ground-truth start time or IoU with the ground-truth start-end window. We also report the average recall (R) and average precision (P) across IoU thresholds of {0.3, 0.5, 0.7, 0.9}.

Implementation details. Unless stated otherwise, for all models, we use the speech transcripts (ASR) and visual features extracted as explained in Section 3.2. By default, each model is taken from the corresponding official implementation, and all model hyper-parameters are set according to the original papers. We use the Adam optimizer [39] for training and select the final model based on the best validation performance. Our experiments are run on 8 NVIDIA A100 80GB GPUs. More details are included in Appendix Section D.

Video chapter generation

In this Section, we study the task of video chapter generation that requires temporally segmenting the video and generating a chapter title for each segment.

Method Modalities

Pretraining Data Finetuned S B1 B2 B3 B4 C M RL Text tiling [START_REF] Marti | Text tiling: Segmenting text into multi-paragraph subtopic passages[END_REF] + Random Speech ∅ ✗ 0.4 0.6 0.2 0.1 0.0 0.8 0.7 0.6 Text tiling [START_REF] Marti | Text tiling: Segmenting text into multi-paragraph subtopic passages[END_REF] + LLaMA [START_REF] Touvron | LLaMA: Open and efficient foundation language models[END_REF] Speech Text mixture ✗ 0.2 0.4 0.1 0.1 0.0 0.5 0.3 0.4 Shot detect [START_REF] Tomar | Converting video formats with ffmpeg[END_REF] + BLIP-2 [START_REF] Li | BLIP-2: Bootstrapping languageimage pre-training with frozen image encoders and large language models[END_REF] Visual 129M image-texts ✗ 0.6 0.7 0.3 0.1 0.1 0.2 0.6 0.8 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Speech+Visual C4 + HowTo100M ✗ 0.1 0.1 0.0 0.0 0.0 0.1 0.1 0.1 PDVC [START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF] Visual ∅ ✓ 6.8 9.4 3.7 1.4 0.9 35.8 9.4 11.4 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Speech C4 ✓ 10.2 9.5 6.7 4.0 2.7 48.8 8.5 11.0 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Speech C4 + HowTo100M ✓ 10.5 9.9 7.0 4.2 2.9 50.7 8.7 11.4 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Visual C4 ✓ 3.1 2.3 1.5 0.6 0.5 10.9 2.2 2.9 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Visual C4 + HowTo100M ✓ 5.5 4.5 2.8 1.2 0.9 21.1 4.1 5.5 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Speech+Visual C4 ✓ 10.6 9.9 7.0 4.2 2.8 51.3 8.8 11.6 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Speech+Visual C4 + HowTo100M ✓ 11.4 10.9 7.7 4.6 3.1 55.7 9.5 12.6

Table 3: Video chapter generation (global metrics) on VidChapters-7M test set. Here, finetuned refers to finetuning on the VidChapters-7M train set, and speech refers to transcribed speech (ASR).

Method Modalities

Pretraining Data Finetuned R@5s R@3s R@0.5 R@0.7 P@5s P@3s P@0.5 P@0.7 Text tiling [ Models. For the video chapter segmentation subtask, we evaluate two zero-shot approaches (i.e., that are not trained on VidChapters-7M): speech text tiling [START_REF] Marti | Text tiling: Segmenting text into multi-paragraph subtopic passages[END_REF], which detects subtopic shifts based on the analysis of lexical co-occurrence patterns, and a visual scene change detection algorithm [START_REF] Tomar | Converting video formats with ffmpeg[END_REF] based on the sum of absolute differences. To derive zero-shot baselines for the full video chapter generation task, we combine text tiling and shot detection with various alternatives that can generate text given text or visual input: a random baseline that predicts a random speech sentence spoken inside the predicted boundaries, LLaMA-7B [START_REF] Touvron | LLaMA: Open and efficient foundation language models[END_REF] (prompted to summarize the speech transcript spoken inside the predicted boundaries) and BLIP-2 [START_REF] Li | BLIP-2: Bootstrapping languageimage pre-training with frozen image encoders and large language models[END_REF] (prompted to describe the middle video frame of the predicted segment). Finally, we also train and evaluate two state-of-the-art end-to-end dense video captioning models on VidChapters-7M: PDVC [START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF] which consists of a visual-only DETR-style [START_REF] Carion | End-to-end object detection with transformers[END_REF] architecture and Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] which is a multi-modal sequence-to-sequence model pretrained on the C4 text corpus [START_REF] Raffel | Exploring the limits of transfer learning with a unified text-to-text transformer[END_REF] and on narrated videos with ASR (e.g., YT-Temporal-1B [START_REF] Zellers | MERLOT Reserve: Neural script knowledge through vision and language and sound[END_REF]). For Vid2Seq, we also report zero-shot results after pretraining on narrated videos without finetuning on VidChapters-7M.

Implementation details. We use the text tiling implementation from the NLTK library [9] which tokenizes the text into pseudosentences of size 50. We use the shot detection software from the FFMPEG library [START_REF] Tomar | Converting video formats with ffmpeg[END_REF] with a confidence threshold of 0.7. For BLIP-2, we use the 3.4B-parameter variant with FLAN-T5-XL [START_REF] Wei | Finetuned language models are zero-shot learners[END_REF] and CLIP ViT-L/14 [START_REF] Dosovitskiy | An image is worth 16x16 words: Transformers for image recognition at scale[END_REF][START_REF] Radford | Learning transferable visual models from natural language supervision[END_REF]. We reimplement Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] (originally released in Jax) in PyTorch, use T5-Base pretrained on C4 [START_REF] Raffel | Exploring the limits of transfer learning with a unified text-to-text transformer[END_REF] for initialization and pretrain Vid2Seq on HowTo100M [64]. More details are included in Appendix Section D.

Results. We report the results for video chapter generation using global metrics and localizationonly metrics in Tables 3 and4, respectively. We observe that models trained on VidChapters-7M outperform zero-shot baselines, demonstrating the effectiveness of training on VidChapters-7M. In particular, PDVC [START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF] has the best precision and Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] achieves the best results in terms of overall generation and recall. We also find that Vid2Seq's speech-only mode outperforms its visual-only mode and that using both speech and visual inputs leads to the best performance. This demonstrates that video chapter generation is a multi-modal task. Finally, we observe that pretraining using ASR in narrated videos from HowTo100M [64] improves the video chapter generation performance of the Vid2Seq model. Specifically, pretraining on HowTo100M is more beneficial for vision-aware models than for the speech-only model.

Method Modalities

Pretraining Data Finetuned R@10s R@5s R@3s R@1s R@0.3 R@0.5 R@0.7 R@0.9 Random Speech ∅ ✗

Video chapter generation given ground-truth boundaries

In this Section, we study the task of generating chapter titles provided correct temporal boundaries of video chapters. This task is a simplification of the previously studied task where we assume perfect temporal segmentation. We adopt the same models and implementation details as previously introduced in Section 4.1.

Results. We report results for video chapter generation given ground-truth boundaries in Table 5. Similar to the full video chapter generation task, we observe that solving the task without training on VidChapters-7M is hard. Indeed, LLaMA [START_REF] Touvron | LLaMA: Open and efficient foundation language models[END_REF] struggles to summarize the speech content into a chapter title and underperforms the random baseline. Furthermore, BLIP-2 [START_REF] Li | BLIP-2: Bootstrapping languageimage pre-training with frozen image encoders and large language models[END_REF] slightly improves over the random baseline. In addition, Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] in zero-shot mode underperforms the random baseline due to the large domain gap between ASR and chapter titles (see Section 3.3). In comparison, the performance of models trained on VidChapters-7M is significantly higher. Moreover, Vid2Seq's speech-only mode outperforms its visual-only mode, and using both speech and visual inputs is beneficial, confirming the benefit of multi-modal reasoning for the task of generating chapter titles. Finally, pretraining on narrated videos from HowTo100M [64] improves the performance of the Vid2Seq model on VidChapters-7M.

Video chapter grounding

In this Section, we study the task of video chapter grounding that requires a model to temporally localize a chapter start time (or start-end window) given an annotated chapter title (query). Hence, compared to the video chapter generation task, we here assume chapter titles to be given and focus on the temporal chapter localization only.

Models. We evaluate three zero-shot alternatives: a random baseline that randomly picks the timestamps of a speech sentence in the video, a BERT [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF] baseline that picks the timestamps of the speech sentence that has the closest text embedding with the queried chapter title, and a CLIP [START_REF] Radford | Learning transferable visual models from natural language supervision[END_REF] baseline picking the frames where the query-frame similarity score drops from the highest scoring frame by a certain threshold ϵ. We also train and evaluate on VidChapters-7M a state-of-the-art end-to-end video grounding model: Moment-DETR [START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF] which is designed for moment retrieval based on visual inputs. Furthermore, we report zero-shot performance of Moment-DETR obtained with the model checkpoint from Lei et al. [START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF] pretrained on 5.4K narrated videos with ASR from the QVHighlights dataset [START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF].

Implementation details. We use the [CLS] token sequence embedding for the BERT baseline and a threshold of ϵ = 0.05 for the CLIP baseline. More details are provided in Appendix Section D. V 129M image-texts 0.6 1.0 0.5 8.9 5.5 0.2 0.1 0.2 3.1 13.7 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] V C4 + VC (ASR) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.8 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] V C4 + VC (Chap.) 0.7 1.1 0.5 21.3 8.6 1.5 1.9 0.6 18.9 10.4 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] T+V C4 + HTM 0.0 0.1 0.0 0.5 0.6 0.0 0.0 0.0 0.5 1.0 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] T+V C4 + VC (ASR) 0.1 0.1 0.0 1.1 0.9 0.0 0.0 0.0 0.7 0.6 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] T+V C4 + VC (Chap.) 0.1 0.2 0.1 0.7 1.4 0.7 1.1 0. Results. We report results for the video chapter grounding task in Table 6. We first observe that the simple zero-shot baselines based on ASR can decently find start times, but struggle to predict start-end windows due to the important domain gap between ASR and video chapters (see Section 3.3). The CLIP [START_REF] Radford | Learning transferable visual models from natural language supervision[END_REF] baseline slightly underperforms the BERT baseline [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF] at retrieving start times, but is much better at finding start-end windows. Furthermore, the Moment-DETR model [START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF] trained on VidChapters-7M outperform the zero-shot baselines for both localization of start times and start-end windows, which further demonstrates the effectiveness of training on VidChapters-7M. Finally, we note that Moment-DETR cannot handle speech inputs, but hope that our results showing the benefit of this modality on other tasks in VidChapters-7M will foster research in the localization of language queries in untrimmed videos using multi-modal inputs (vision and speech transcripts).

Transfer learning on dense video captioning

In this Section, we investigate the pretraining of video-language models on our new VidChapters-7M.

To this end, we adopt video chapter generation models trained on VidChapters-7M (see Section 4.1) to the tasks of dense video captioning with or without finetuning.

Datasets. We use two dense video captioning datasets. YouCook2 [START_REF] Zhou | Towards automatic learning of procedures from web instructional videos[END_REF] has 2K untrimmed videos of cooking procedures. On average, each video lasts 320s and is annotated with 7.7 temporally-localized sentences. ViTT [START_REF] Huang | Multimodal pretraining for dense video captioning[END_REF] was created to better reflect the distribution of instructional videos in the wild compared to YouCook2, and consists of 8K untrimmed instructional videos. On average, each video lasts 250s and is annotated with 7.1 temporally-localized short tags. For both datasets, we extract speech transcripts and visual features as described in Section 3.2, and follow the standard splits for training, validation and testing. Note that we only use videos available on YouTube at the time of the work, resulting in 10 to 20% less videos than in the original datasets.

Implementation details. See Section 4.1 and Appendix Section D.

Results after finetuning. In Table 7, we show that pretraining for video chapter generation on VidChapters-7M greatly improves the downstream dense video captioning performance compared to training from scratch or pretraining only with ASR data as done in previous work [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF]. We also find that pretraining both on HowTo100M [64] and VidChapters-7M results in the best overall performance. In particular, the Vid2Seq model pretrained on both HowTo100M and VidChapters-7M largely improves the state of the art on both the YouCook2 and ViTT benchmarks. In detail, on the YouCook2 benchmark, in the setting with C4 + HowTo100M pretraining, we observe that a boost of about 4.9 points in CIDEr is obtained with our reimplementation of Vid2Seq, and that 14.0 additional points in CIDEr are obtained by pretraining on VidChapters-7M. Finally, we report the results of the Vid2Seq model after pretraining on different fractions of VidChapters-7M for a fixed number of iterations. We construct these subsets such that larger subsets include the smaller ones. These results suggest that the scale of the chapter dataset is an important factor in the downstream dense video captioning performance. We conclude that VidChapters-7M opens a promising avenue for multi-modal pretraining. We further show qualitative examples of dense video captioning in Appendix Section B.

Zero-shot dense video captioning. In Table 8, we report results obtained by directly applying video chapter generation models trained on VidChapters-7M for dense video captioning without finetuning for this task. As far as we know, our work is the first to explore this challenging zero-shot setting where no manual annotation of dense video captions is used for training. The Vid2Seq model trained only using ASR data underperforms the random baseline, due to the large domain difference between speech transcripts and dense captions [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF]. In the visual-only setting, the variant trained on chapter annotations is better than the variant trained on ASR annotations. In the visual+speech settings, only using chapter annotations does not perform well, as training only on chapters (i.e., without speech) does not enable the model to learn how to use the input speech modality at inference. However, using both ASR and chapter annotations results in a largely better zero-shot dense video captioning performance and outperforms all baselines not trained on VidChapters-7M, demonstrating the complementary nature of the ASR and chapters annotations. Finally, we also observe the benefits of increasing the size of the pretraining dataset of chapters in this setting.

Conclusion, Limitations, and Societal Impacts

In this work, we presented VidChapters-7M, a large-scale dataset of user-chaptered videos. Furthermore, we evaluated a variety of baselines on the tasks of video chapter generation with and without ground-truth boundaries and video chapter grounding. Finally, we investigated the potential of VidChapters-7M for pretraining video-language models and demonstrated improved performance on the dense video captioning tasks. VidChapters-7M thus provides a new resource to the research community that can be used both as a benchmark for the video chapter generation tasks and as a powerful means for pretraining generic video-language models.

Limitations. As it is derived from YT-Temporal-180M [START_REF] Zellers | MERLOT: Multimodal neural script knowledge models[END_REF], VidChapters-7M inherits the biases in the distribution of video categories reflected in this dataset.

Societal Impacts. The development of video chapter generation models might facilitate potentially harmful downstream applications, e.g., video surveillance. Moreover, models trained on VidChapters-7M might reflect biases present in videos from YouTube. It is important to keep this in mind when deploying, analysing and building upon these models. Chapters depict visual events (e.g., the mini chicken burgers that appear in the second video), conversations (see the last video), or events in the raw audio (e.g., the sound of the crinkly plastic bag in the penultimate video) in various scenarios.

D.1 Tagging for ethical considerations

In Section 3.3, we explained how we tag videos for ethical considerations. We here give additional details about this procedure. For the NSFW visual content detector [START_REF] Schuhmann | LAION-5B: An open large-scale dataset for training next generation image-text models[END_REF], we compute the NSFW score at every frame (at 1 FPS) and tag videos with an average score above 0.5. For the toxic content detection model [START_REF] Hanu | Unitary team[END_REF], we compute the toxicity score at every chapter title / ASR sentence and tag videos where the chapter titles / ASR have an average toxicity score above 0.5.

D.2 Video chapter generation and dense video captioning

LLaMA [START_REF] Touvron | LLaMA: Open and efficient foundation language models[END_REF]. We use the following prompt: Summarize the following speech transcript in a chapter title. Transcript: <ASR> Chapter title: where the ASR is the concatenation of all speech sentences spoken during a given video segment.

BLIP-2 [START_REF] Li | BLIP-2: Bootstrapping languageimage pre-training with frozen image encoders and large language models[END_REF]. We use the following prompt: Summarize the image in a chapter title. Chapter title:, and use the middle frame of the predicted video segment.

Input Frames

Vid2Seq (HTM)

Vid2Seq (HTM +VC)

Heat a pan over medium heat.

Heat a pan with olive oil.

Add 2 tbsp of olive oil to the pan.

Add onions to the pan and saute.

Add olive oil to the pan.

Add onions to the pan.

Add red pepper sweet potatoes and water to the pan.

Add red pepper to the pan.

Add red pepper and water to the pan.

Add sweet potatoes and water to the pan.

Cover the pan with a lid.

Cook the sweet potatoes in the pan.

Input Speech

Okay, so let's get started. So I'm going to heat our pan over medium heat. Once again, keep the temperature low because we are using olive oil. You don't want to cook olive oil over too high of heat.

We'll add in two teaspoons of olive oil to coat the We'll let that heat up, then we're going to add the other ingredients. So come on over.

Okay, now that the pan has been heated over medium heat, let's add in the onions and let's saute these for about two minutes. Get them soft, then we'll add in the other ingredients.

Now that the onions are nice and sauteed and smelling delicious, let's add in our red pepper.

And then we'll add in our sweet potatoes, and then about a tablespoon of water.

So what we're hoping for in that 15 minutes is that these sweet potatoes will become soft and ready to eat.

Whatever you want to call it, it smells delicious, it looks pretty cool. Add this as a side dish to any protein source, so a steak, chicken, or have it in the morning with your eggs and you're good to go.

Input Frames

Vid2Seq (HTM)

Vid2Seq (HTM +VC)

Peel rinse and cut the potatoes.

Boil the potatoes in water.

Boil the potatoes in water.

Peel rinse and cut the potatoes. Boil the potatoes in water and salt the water.

Drain the potatoes and dry them out.

Drain the potatoes and dry them out in a pot.

Add milk and mash the potatoes.

Add cream and milk to the potatoes.

Add fresh herbs parsley and butter to the mashed potatoes.

Add chives parsley and butter to the potatoes.

Input Speech

When

Ground -Truth

Add some chopped white onions in a pan under medium heat.

Add a spoon of water.

Cook with lid on.

Add in red pepper and sweet potatoes.

Season it with salt and black pepper.

Ground -Truth

Peel and cut potatoes into chunks.

Drain and dry the potatoes.

Mash the potatoes well with a hand masher.

Put in cold water and cook to a boil and salt the water.

Add milk and stir the potatoes.

Season the potatoes with some chopped parsley leaves. We find that the model pretrained on VidChapters-7M "Vid2Seq (HTM+VC)" is more accurate and less prone to hallucination. For instance, in the first example (top), the non-VC-pretrained model "Vid2Seq (HTM)" predicts "Add red pepper sweet potatoes and water to the pan." before the sweet potatoes are actually thrown into the pan. In the second example (bottom), the non-VC-pretrained model "Vid2Seq (HTM)" predicts the event "Boil the potatoes in water." twice and predicts the event "Add chives parsley and butter to the potatoes." before it actually happens. The VC-pretrained model "Vid2Seq (HTM+VC)" produces more accurate predictions.

PDVC [START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF]. We use PDVC's official codebase. PDVC includes a caption decoder that relies on dataset-specific word vocabularies. To adapt PDVC to VidChapters-7M/YouCook2/ViTT, we construct a vocabulary made with all words that appear at least 50/2/3 times in the dataset (33,598/3,815/1,607 words). For transfer learning from VidChapters-7M to YouCook2/ViTT, we initialize the downstream dataset-specific word embedding layer with the weights of the corresponding word embedding in the pretrained model. We subsample or pad the sequence of frames to 100 frames. For all datasets, we use 100 queries and train with a constant learning rate of 5e -5 , weight decay 1e -4 and batch size 1 on an NVIDIA V100 32GB (as the official codebase is not compatible with higher batch sizes or multi-gpu training) . We train on VidChapters-7M/YouCook2/ViTT for 5/30/30 epochs. The training on VidChapters-7M lasts about a week. token prediction objective in the chapter sequence and the denoising objective in the speech sequence. Finetuning on YouCook2/ViTT is done with the next token prediction objective in the dense video captioning sequence and the denoising objective in the speech sequence. We subsample or zero-pad the sequence of frames to 100 frames. The text encoder and decoder sequence are truncated or padded to 1000 and 256 tokens, respectively. For all datasets, we use a learning rate of 3e -4 warmed up linearly (from 0) for the first 10% of iterations and following a cosine decay (down to 0) for the remaining 90%. We train for 6/10/40/20 epochs on HowTo100M/VidChapters-7M/YouCook2/ViTT. We use a batch size of 64 videos split on 8 NVIDIA A100 80GB for HowTo100M/VidChapters-7M, and 16 videos split on 8 NVIDIA V100 32GB for YouCook2/ViTT. The training on HowTo100M or VidChapters-7M takes about 2 days.

D.3 Video chapter generation with ground-truth boundaries

LLaMA [START_REF] Touvron | LLaMA: Open and efficient foundation language models[END_REF] and BLIP-2 [START_REF] Li | BLIP-2: Bootstrapping languageimage pre-training with frozen image encoders and large language models[END_REF]. See Section D.2.

Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF]. To adapt the model pretrained on HowTo100M (see Section D.2) to video chapter generation with ground-truth boundaries, we remove the model weights corresponding to the time tokens (in the token embedding layers and the token prediction layer). We train for 20 epochs on VidChapters-7M using the next token prediction objective in the sequence of tokens corresponding to a single chapter title. We construct training batches by sampling a chapter title with its associated video clip at each iteration (i.e., an epoch corresponds to seeing one chapter title for all videos). The text encoder and decoder sequence are truncated or padded to 256 and 32 tokens, respectively. We use a learning rate of 3e -4 warmed up linearly (from 0) for the first 10% of iterations and following a cosine decay (down to 0) for the remaining 90%. We use a batch size of 512 videos split on 8 NVIDIA A100 80GB for VidChapters-7M. The training takes about a day.

D.4 Video chapter grounding

Moment-DETR [START_REF] Lei | Detecting moments and highlights in videos via natural language queries[END_REF]. We use Moment-DETR's official codebase. We train with the AdamW optimizer [START_REF] Loshchilov | Decoupled weight decay regularization[END_REF], a constant learning rate of 3e -4 , and a batch size of 256 videos split on 8 NVIDIA A100 80GB. We use a FPS of 1/3 and subsample or zero-pad the sequence of frames to 1200 frames. We use a maximum number of text query tokens of 77. We train for 50 epochs on VidChapters-7M, where an epoch corresponds to seeing one chapter title for all videos, which takes about 2 days.

E Video chapter generation results split by language

We report video chapter generation results on the VidChapters-7M dataset split by language for both English and German in Tables 9 and10, respectively. We find that training on VidChapters-7M is beneficial for both languages. Interestingly, pretraining on HowTo100M (which is a dataset in English) improves results on English as well as German. We also observe that the quantitative results in German are lower than in English. Finally, we report results of the Vid2Seq model with the multi-lingual language model mT5 [START_REF] Xue | mT5: A massively multilingual pre-trained text-to-text transformer[END_REF] pretrained on the multi-lingual dataset mC4 [START_REF] Xue | mT5: A massively multilingual pre-trained text-to-text transformer[END_REF]. We Q14. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, (a) Are there guarantees that they will exist, and remain constant, over time? (b) Are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created)? (c) Are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.

-

We do not distribute videos of our dataset to respect YouTube user privacy and to limit our storage budget. Instead, we provide video URLs ("url", Q8) that point to videos hosted on YouTube servers. In response to sub-questions: (a) These video servers ensure stable access unless the YouTube user deletes their video. (b) Yes, YouTube archives all the metadata of submitted videos. For videos, YouTube only archives the URL and not the media content, giving full control of accessibility to the users. (c) All video URLs are freely accessible. It is unlikely for video servers to restrict access in the future, given their free accessibility over the past decade.

Q15. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals non-public communications)? If so, please provide a description. -No, the videos included in VidChapters-7M do not cover topics that may be considered confidential. All videos were publicly shared on YouTube prior to inclusion in VidChapters-7M.

Q16. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.

-

The scale of VidChapters-7M means that we are unable to manually verify the contents of all videos and chapters. However, YouTube removes videos that contain offensive content or do not follow their community guidelines. Furthermore, we employed additional mitigation techniques on VidChapters-7M:

(a) We tagged all instances whose video frames were predicted as NSFW by an off-the-shelf detector [START_REF] Schuhmann | LAION-5B: An open large-scale dataset for training next generation image-text models[END_REF]. (b) We tagged all instances whose chapter titles or speech transcripts were predicted as toxic by a language model [START_REF] Hanu | Unitary team[END_REF].

Q17. Does the dataset relate to people? If not, you may skip remaining questions in this section.

-The dataset pertains to people in that people upload videos to YouTube and write descriptions that include chapter annotations. Furthermore, most videos in VidChapters-7M have people speaking and/or appearing.

Q18. Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. -VidChapters-7M does not explicitly identify any subpopulations. Since most videos contain people and chapters are free-form natural language written by YouTube users, it is possible that some chapters may identify people appearing in individual videos as part of a subpopulation.

Q19. Is it possible to identify one or more natural persons, either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how. -Yes, our data includes celebrities, or other YouTube-famous people. All of the videos that we use are of publicly available data, following the Terms of Service (https://www.youtube. com/static?template=terms) that users agreed to when uploading to YouTube. Q20. Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description. -This is highly unlikely, as YouTube removes videos that contain offensive content or do not follow their community guidelines.

Q21. Any other comments? -No.

Collection Process

Q22. How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. -See Q7 for an explanation of how the candidate video IDs were chosen. These video IDs were provided by the YT-Temporal-180M dataset providers [START_REF] Zellers | MERLOT: Multimodal neural script knowledge models[END_REF] and collected via the YouTube API. The "video_id" and "URL" are directly observable. The "chapters" are extracted from the YouTube description which is directly observable. The "asr" is obtained by applying the Whisper-Large-V2 model [START_REF] Radford | Robust speech recognition via large-scale weak supervision[END_REF] to the directly observable audio from the video. We found this model to provide higher-quality transcriptions compared to the YouTube API on several data samples from VidChapters-7M.

Q23. What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated? -We collected all data using compute resources provided by IDRIS, under the allocation 2023-A0131011670 made by GENCI. The code for querying APIs, extracting ASR, and filtering data are implemented in Python. The code was validated by checking several data samples from VidChapters-7M.

Q24. If the dataset is a sample from a larger set, what was the sampling strategy? -See Q7.

Q25. Who was involved in data collection process (e.g., students, crowd-workers, contractors) and how were they compensated (e.g., how much were crowd-workers paid)?

-Our data collection pipeline is fully automatic and does not require any human annotators. YouTube users have uploaded videos whose metadata is a part of VidChapters-7M -we did not directly interact with these users.

Q26.

Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)?

If not, please provide a description of the timeframe. -VidChapters-7M contains videos that were uploaded to YouTube between 2005-2022. We collected all data in early 2023, which we used to conduct experiments for our NeurIPS 2023 submission.

Q27. Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.

-

We did not conduct a formal ethical review process via institutional review boards. However, as described in Section 3.3 and Q16 we employed several filtering mechanisms to tag instances that could be problematic.

Q28. Does the dataset relate to people? If not, you may skip remaining questions in this section.

-Yes, see Q17.

Q29. Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?

-We collected data submitted by YouTube users indirectly through the YouTube API. However, users agree with YouTube's Terms of Service regarding the redistribution of their data by YouTube.

Q30. Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself. -No. YouTube users are not required to share their personal contact information (email, phone numbers, etc.). Hence, the only way to notify the authors of VidChapters-7M videos is by commenting on their videos. This is practically difficult to do manually and will be classified as spam and blocked by YouTube if attempted to programmatically write a templated comment to millions of users.

Q31. Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented. -Users did not explicitly consent to the use of their data in our dataset. However, by uploading their data on YouTube, they consent that it would appear on the YouTube plaform and will be accessible via the official YouTube API (which we use to collect VidChapters-7M).

Q32. If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate). -Users have full control over the presence of their data in our dataset. If users wish to revoke their consent, they can delete the underlying YouTube video -it will be automatically removed from VidChapters-7M since we distributed videos as URLs. Moreover, we provide an opt-out request form on our dataset website for anybody to request removal of an individual instance if it is potentially harmful (e.g. NSFW, violates privacy, harmful stereotypes, etc.).

Q33.

Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. -No.

Q34. Any other comments?

-No.

Preprocessing, Cleaning, and/or Labeling Q35. Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section. -We converted chapter timestamps in HH:MM:SS format to seconds. Refer to Section 3.1 for more details. We also extracted speech transcripts and visual features (see Section 3.2). Finally, we tagged some instances with a focus on ethical considerations, see Q16 for more details.

Q36. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the "raw" data. -Yes, the raw descriptions from which chapters are extracted are also released on the dataset website [START_REF]VidChapters-7M project webpage[END_REF].

Q37. Is the software used to preprocess/clean/label the instances available? If so, please provide a link or other access point. -Yes, the data preprocessing code is open-sourced and accessible from the dataset website [START_REF]VidChapters-7M project webpage[END_REF].

Q38. Any other comments?

-No.

Uses

Q39. Has the dataset been used for any tasks already? If so, please provide a description.

-We have used our dataset to train deep neural networks that perform video chapter generation, and that can be transferred to dense video captioning tasks (see Sections 4.1 and 4.4). We also trained models for video chapter generation with ground-truth boundaries and video chapter grounding (see Sections 4.2 and 4.3).

Q40. Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point.

-

We do not maintain such a repository. However, citation trackers like Google Scholar and Semantic Scholar would list all future works that cite our dataset.

Q41. What (other) tasks could the dataset be used for? -We anticipate that the dataset could be used for a variety of video-and-language tasks, such as text-to-video retrieval.

Q42. Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms? -This is very difficult to anticipate. Future users of our dataset should be aware of YouTube's user demographics which might subtly influence the types of videos, languages, and ideas that are present in the dataset. Also, note that our dataset is mainly composed of English videos, hence models trained on this dataset might perform worse on videos in other languages. Q43. Are there any tasks for which the dataset should not be used? If so, please provide a description. -Broadly speaking, our dataset should only be used for non-commercial academic research. Our dataset should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or making decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of our dataset are restricted -it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.

Q44. Any other comments? -No.

Distribution

Q45. Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. -Yes, our dataset is publicly available.

Q46. How will the dataset will be distributed (e.g., tarball on website, API, GitHub) Does the dataset have a digital object identifier (DOI)? -We distribute our dataset as JSON/PICKLE files containing annotations. Users will have to download the videos by themselves by using our data collection code. All uses of VidChapters-7M should cite the paper as the reference.

Q47. When will the dataset be distributed? -The dataset is publicly available as of September 2023.

Q48. Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.

Figure 1 :

 1 Figure 1: A video with user-annotated chapters in VidChapters-7M: the video is temporally segmented into chapters, which are annotated with a chapter title in free-form natural language.

37th

  Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

Figure 2 :

 2 Figure 2: Illustration of the three tasks defined for VidChapters-7M.

Figure 4 :

 4 Figure 4: Additional examples of videos with user-annotated chapters in VidChapters-7M:Chapters depict visual events (e.g., the mini chicken burgers that appear in the second video), conversations (see the last video), or events in the raw audio (e.g., the sound of the crinkly plastic bag in the penultimate video) in various scenarios.

Figure 5 :

 5 Figure 5: Examples of video chapter generation using the Vid2Seq model with different input modalities compared with ground-truth on the test set of VidChapters-7M. The first exampleshows that the Vid2Seq variant with both speech and visual modalities "Vid2Seq (HTM+VC)" can predict the structure of the input video without the ASR input, unlike the Vid2Seq speech-only variant "Vid2Seq (HTM+VC, no vision)". The second example shows that the Vid2Seq variant with both speech and visual modalities "Vid2Seq (HTM +VC)" can effectively leverage speech cues to detect the names of the depicted and discussed shoes, unlike the Vid2Seq visual-only variant "Vid2Seq (HTM+VC, no speech)".

Figure 6 :

 6 Figure 6: Examples of dense event captioning of the Vid2Seq model pretrained on VidChapters-7M (vs. not pre-trained), compared with ground-truth, on the validation set of YouCook2.We find that the model pretrained on VidChapters-7M "Vid2Seq (HTM+VC)" is more accurate and less prone to hallucination. For instance, in the first example (top), the non-VC-pretrained model "Vid2Seq (HTM)" predicts "Add red pepper sweet potatoes and water to the pan." before the sweet potatoes are actually thrown into the pan. In the second example (bottom), the non-VC-pretrained model "Vid2Seq (HTM)" predicts the event "Boil the potatoes in water." twice and predicts the event "Add chives parsley and butter to the potatoes." before it actually happens. The VC-pretrained model "Vid2Seq (HTM+VC)" produces more accurate predictions.

Figure 8 :

 8 Figure 8: Average visual-text similarity between chapter titles and the corresponding video frames as measured by CLIP cosine similarity (rescaled between 0 and 100) in VidChapters-7M.

Table 1 :

 1 Comparison of VidChapters-7M with existing datasets.

	Dataset	Number of videos	Video duration (min)	Number of descriptions	Annotations
	HowTo100M [64]	1M	7	136M	Speech transcripts
	YT-Temporal-1B [118]	19M	6	∼ 900M	Speech transcripts
	HD-VILA-100M [108]	3M	7	103M	Speech transcripts
	ActivityNet Captions [42]	20K	3	100K	Dense Captions
	YouCook2 [127]	2K	6	15K	Dense Captions
	ViTT [36]	8K	4	56K	Dense Captions
	Ego4D [29]	10K	23	4M	Dense Captions
	VidChapters-7M (Ours)	817K	23	7M	Speech transcripts + User-annotated Chapters

Table 4 :

 4 Video chapter generation (segmentation metrics) on VidChapters-7M test set.

	32]	Speech	∅	✗	9.4	5.8	23.6	8.9	12.6 7.9 26.0	8.8
	Shot detect [92]	Visual	∅	✗	31.2 27.4 24.9 12.5 33.2 29.7 18.0	8.7
	Vid2Seq [114] Speech+Visual C4 + HowTo100M	✗	10.7 9.5	5.8	0.2	23.3 18.5 1.9	0.8
	PDVC [101]	Visual	∅	✓	21.1 17.8 31.2 22.5 45.3 40.2 47.2 26.9
	Vid2Seq [114]	Speech	C4	✓	37.8 29.5 44.6 26.1 29.0 23.0 38.0 23.4
	Vid2Seq [114]	Speech	C4 + HowTo100M	✓	36.7 28.9 46.5 27.2 29.5 23.3 40.4 24.8
	Vid2Seq [114]	Visual	C4	✓	35.3 26.4 23.6	8.7	17.9 13.6 17.2	7.1
	Vid2Seq [114]	Visual	C4 + HowTo100M	✓	33.5 25.0 33.0 14.5 19.5 14.7 26.2 12.5
	Vid2Seq [114] Speech+Visual	C4	✓	36.3 28.6 45.8 26.9 29.9 23.8 40.9 24.9
	Vid2Seq [114] Speech+Visual C4 + HowTo100M	✓	36.4 28.5 48.2 28.5 30.3 24.0 43.1 26.4

Table 5 :

 5 Chapter title generation given ground-truth boundaries on VidChapters-7M test set.

Table 6 :

 6 Video chapter grounding on VidChapters-7M test set.

	.1	1.8 1.2 0.6	0.7	0.3	0.1	0.0

Qualitative examples. See Appendix Section B.

Table 7 :

 7 Comparison with the state of the art on the YouCook2 and ViTT dense video captioning benchmarks. T: Transcribed speech, V: Visual, HTM: HowTo100M [64], VC: VidChapters-7M, Chap.: Chapters. † denote results of our experiments.

	Method	Modalities		Pretraining Data	S	YouCook2 (val) C M R	P	S	ViTT (test) C M R	P
	PDVC [101]	V		∅		4.4 22.7 4.7 -------
	E2ESG [130]	T+V		C4 + WikiHow	-25.0 3.5 20.7 20.6 -25.0 8.1 32.2 32.1
	Vid2Seq [114]	T+V		C4 + HTM	8.3 48.3 9.5 27.1 27.0 -----
	Vid2Seq [114]	T+V		C4 + YT-Temporal-1B	7.9 47.1 9.3 27.9 27.8 13.5 43.5 8.5 42.6 46.2
	PDVC †	V		∅		4.8 28.8 5.8 22.6 33.1 9.4 40.6 16.5 19.2 37.4
	PDVC †	V		VC (Chap.)	5.9 34.7 7.5 28.8 36.4 10.1 41.5 16.1 21.3 37.2
	Vid2Seq †	T+V		C4 + HTM	8.6 53.2 10.5 29.2 26.2 14.1 44.8 8.7 43.8 44.5
	Vid2Seq †	T+V		C4 + VC (ASR+Chap.)	9.8 62.9 11.7 32.5 30.1 15.1 50.9 9.6 45.1 46.7
	Vid2Seq †	T+V		C4 + HTM + VC (ASR)	8.4 50.1 10.3 29.7 26.3 14.3 45.6 8.8 43.7 44.9
	Vid2Seq †	T+V	C4 + HTM + 1% of VC (ASR+Chap) 8.8 52.7 10.4 29.3 27.6 13.5 41.6 8.2 44.7 42.1
	Vid2Seq †	T+V	C4 + HTM + 10% of VC (ASR+Chap.) 9.9 63.9 12.1 32.4 31.4 14.5 47.4 9.2 45.3 45.9
	Vid2Seq †	T+V		C4 + HTM + VC (ASR+Chap.)	10.3 67.2 12.3 34.0 31.2 15.0 50.0 9.5 45.5 46.9
	Method			Modalities	Pretraining Data		YouCook2 (val) S C M R	ViTT (test) P S C M R	P
	Text tiling [32] + Random		T	∅		0.3 0.9 0.3 3.8 6.6 0.3 0.6 0.6 11.6 24.4
	Text tiling [32] + LLaMA [93]	T	Text mixture		0.2 0.6 0.2 3.8 6.6 0.2 0.6 0.5 11.6 24.4
	Shot detect [92] + BLIP-2 [51]

Table 8 :

 8 Zero-shot dense video captioning on the YouCook2 and ViTT benchmarks.

	3 14.3 12.8

T: Transcribed speech, V: Visual, HTM: HowTo100M [64], VC: VidChapters-7M, Chap.: Chapters.

To generate the word clouds, we used https://github.com/amueller/word_cloud.

Vid2Seq[START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF]. We reimplement Vid2Seq (originally released in Jax) in PyTorch. For initialization, we use the T5-Base language model pretrained on the C4 text corpus[START_REF] Raffel | Exploring the limits of transfer learning with a unified text-to-text transformer[END_REF]. Vid2Seq is originally pretrained on YT-Temporal-1B[START_REF] Zellers | MERLOT Reserve: Neural script knowledge through vision and language and sound[END_REF] using a generative and denoising objective in the speech sequence. Due to computational limitations, we instead pretrain Vid2Seq on the smaller HowTo100M dataset[64] with the same objectives. Then we train Vid2Seq on VidChapters-7M with the next

Acknowledgements

This work was granted access to the HPC resources of IDRIS under the allocation 2023-A0131011670 made by GENCI. The work was funded by Antoine Yang's Google PhD fellowship, the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), the Louis Vuitton ENS Chair on Artificial Intelligence, the European Regional Development Fund under project IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000468). We thank Jack Hessel and Rémi Lacroix for helping with collecting the dataset, and Antoine Miech for interesting discussions.

Appendix

In this Appendix, we present the following items:

(i) Additional examples from our VidChapters-7M dataset (Section A). (ii) Qualitative examples of video chapter generation and dense video caption prediction (Section B). (iii) Additional data analysis of our VidChapters-7M dataset (Section C). (iv) Additional implementation details (Section D). (v) Video chapter generation results split by language (Section E). (vi) A datasheet [START_REF] Gebru | Datasheets for datasets[END_REF] for VidChapters-7M (Section F). Note that in this datasheet, the hosting, licensing, and maintenance plan of VidChapters-7M is presented.

Note that our code, models and the VidChapters-7M dataset can be found on our website [START_REF]VidChapters-7M project webpage[END_REF].

A Additional examples from VidChapters-7M

In Figure 4, we provide additional examples that complement Figure 1. These examples illustrate the diversity of the data in VidChapters-7M, e.g., our dataset includes review videos, cooking videos, clothing fitting videos, ASMR videos, and videos of conversations. These examples also show the multi-modal nature of the chapter data. Indeed, chapters depict visual events (e.g., the mini chicken burgers that appear in the second video), conversations (see the last video), or events in the raw audio (e.g., the sound of the crinkly plastic bag in the penultimate video) in various scenarios.

B Qualitative examples of video chapter generation and dense video caption prediction

We present qualitative results for video chapter generation and dense video captioning in Figures 5 and6. Compared with the speech-only model, a key advantage of the speech+visual video chapter generation model is that it can generalize to videos that do not have ASR input, as shown in the first example of Figure 5. Compared with the visual-only variant, the multi-modal model can also benefit from speech cues, as seen in the second example in Figure 5. Moreover, we observe that the dense video captioning model pretrained on VidChapters-7M is more accurate and hallucinates less than the variant not pretrained on VidChapters-7M, see Figure 6.

C Additional data analysis of VidChapters-7M

We here complement the analysis of the data in VidChapters-7M provided in Section 3.3. In Figure 7, we show a histogram of the most common chapter titles and word clouds 1 of the chapters titles and ASR content in VidChapters-7M. A few generic chapter titles that outline the structure of the video (e.g., Intro, Introduction, Outro, Conclusion and Start) appear more than 10K times. Besides, we notice that many videos include chapters about Unboxing, Review, or Tips. This is consistent with the fact that there are many vlogs and 'Howto' videos in VidChapters-7M. We also observe that the most common words in the ASR largely differ from the most common words in the chapter titles, which further shows the difference between these two types of data. To further measure the text-video alignment in the VidChapters-7M dataset, we compute the CLIP cosine similarity between chapter titles and their corresponding video frames and plot the resulting distribution in Figure 8. The average similarity score is 54.6%, and less than 1% of the chapters have a visual-text similarity score below 30%. These statistics demonstrate a good video-text alignment in the VidChapters-7M dataset.

D Additional implementation details

In this Section, we present implementation details that complement the information provided in Section 4. We discuss implementation details of our tagging protocol for ethical considerations in

Method Modalities

Pretraining Data Finetuned S B1 B2 B3 B4 C M RL Text tiling [START_REF] Marti | Text tiling: Segmenting text into multi-paragraph subtopic passages[END_REF] + Random Speech ∅ ✗ 0.5 0.8 0.2 0.1 0.0 0.9 0.8 0.7 Text tiling [START_REF] Marti | Text tiling: Segmenting text into multi-paragraph subtopic passages[END_REF] + LLaMA [START_REF] Touvron | LLaMA: Open and efficient foundation language models[END_REF] Speech Text mixture ✗ 0.3 0.5 0.2 0.1 0.0 0.5 0.4 0.4 Shot detect [START_REF] Tomar | Converting video formats with ffmpeg[END_REF] + BLIP-2 [START_REF] Li | BLIP-2: Bootstrapping languageimage pre-training with frozen image encoders and large language models[END_REF] Visual 129M image-texts ✗ 1.3 1.5 0.7 0.4 0.2 4.7 1.4 1.6 PDVC [START_REF] Wang | End-to-end dense video captioning with parallel decoding[END_REF] Visual ∅ ✓ 6.6 9.0 3.8 1.5 1.0 36.0 9.1 11.0 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] Speech+Visual C4 ✓ 10.8 10.3 7.6 4.9 3.4 54.8 9.1 11.9 Vid2Seq [START_REF] Yang | Vid2Seq: Large-scale pretraining of a visual language model for dense video captioning[END_REF] find that this variant performs a bit worse on English but slightly better on German compared to the Vid2Seq variant based on T5 pretrained on the C4 corpus.

F Datasheet for VidChapters-7M

Datasheets for datasets introduced by Gebru et al. [START_REF] Gebru | Datasheets for datasets[END_REF] serve as a medium of communication between the creators and users of a dataset. They effectively consolidate the motivation, creation process, composition, and intended uses of a dataset as a series of questions and answers. In this Section, we provide a datasheet for the VidChapters-7M dataset.

Motivation

Q1. For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.

-

The VidChapters-7M dataset was created to explore the task of video chapter generation, which enables users to quickly navigate to the information of their interest.

Q2. Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? -Five researchers have created VidChapters-7M: Antoine Yang (Inria and DI ENS), Arsha Nagrani (VGG, University of Oxford), Ivan Laptev (Inria and DI ENS), Josef Sivic (CIIRC CTU) and Cordelia Schmid (Inria and DI ENS).

Q3. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. -We collected VidChapters-7M without any monetary costs, since no part of our dataset requires annotations from crowd workers or contractors. This research work was funded by Antoine Yang's Google PhD fellowship, the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), the Louis Vuitton ENS Chair on Artificial Intelligence, the European Regional Development Fund under project IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000468). However, note that this article solely reflects the opinions and conclusions of its authors and not of its funders.

Composition

Q5. What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. -Each instance in VidChapters-7M represents a YouTube video. Q6. How many instances are there in total (of each type, if appropriate)?

-There are 817K instances in VidChapters-7M.

Q7. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). -VidChapters-7M is a small sample drawn from all the data uploaded to YouTube. Millions of videos are uploaded on YouTube every day. We started from a subset of 92 million YouTube video candidates, which consists of videos recommended in videos from the YT-Temporal-180M dataset [START_REF] Zellers | MERLOT: Multimodal neural script knowledge models[END_REF]. We selected the videos from this subset (817K instances) that contain user-annotated chapters. Hence, VidChapters-7M data does not fully represent YouTube.

Q8. What data does each instance consist of? "Raw" data (e.g., unprocessed text or images) or features? In either case, please provide a description. -Each instance in VidChapters-7M consists of four metadata fields:

• "video_id": Unique alphanumeric ID of the video (assigned by YouTube).

• "url": Static URL for downloading the video, e.g., https://www.youtube.com/watch?v=<video_id>.

• "asr": ASR transcripts aligned over time.

• "chapters": Chapters aligned over time.

Q9. Is there a label or target associated with each instance? If so, please provide a description.

-We use the chapters as labels in this work, though it might be also possible to use auxiliary information (like video titles or tags). Q10. Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. -No and yes. No, because all the metadata fields for every instance are filled with valid values.

Yes, because the "url" for some instances may not retrieve the underlying video. This may happen if the YouTube user (author) removes the video from YouTube. Such deletions reduce our dataset size over time, however, video deletions are rare.

Q11. Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? If so, please describe how these relationships are made explicit. -Relationships between individual instances (e.g., videos made by the same creator) are not made explicit in our work, though this is a possibility for future work.

Q12. Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.

-

We randomly split our data into training, validation, and testing sets. The training, validation, and testing sets are meant for training, development, and evaluation, respectively. Q13. Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. -VidChapters-7M is inherently noisy since YouTubers are free to write the chapters that they want.

-Uses of our dataset are subject to YouTube API terms (https://www.youtube.com/ static?template=terms). The data and code are released with an MIT license. Q49. Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. -The videos corresponding to our instances are legally owned by YouTube users. Our dataset users can download them from the URLs we provide in annotation files, but redistributing videos for commercial use is prohibited.

Q50. Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. -No.

Q51. Any other comments?

-No.

Maintenance

Q52. Who will be supporting/hosting/maintaining the dataset? -The authors will maintain the dataset. The dataset is hosted using Inria servers and Google Drive service. All the information about the dataset, including links to the paper, code, and future announcements will be accessible at the dataset website [START_REF]VidChapters-7M project webpage[END_REF].

Q53. How can the owner/curator/manager of the dataset be contacted (e.g., email address)?

-The contact emails of authors are available on the dataset website [START_REF]VidChapters-7M project webpage[END_REF].

Q54. Is there an erratum? If so, please provide a link or other access point.

-There is no erratum for our initial release. We will version all errata as future releases (Q55) and document them on the dataset website [START_REF]VidChapters-7M project webpage[END_REF].

Q55. Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)? -We will update our dataset once every year and announce it on the dataset website [START_REF]VidChapters-7M project webpage[END_REF]. These future versions would remove instances that were requested to be removed via the opt-out form (Q32).

Q56. If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. -Rather than directly distributing videos, we distribute URLs that point to the original videos uploaded by YouTube users. This means that users retain full control of their data -any post deleted from YouTube will be automatically removed from VidChapters-7M (see also Q10, Q14, Q31).

Q57. Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users.

-A new version release of VidChapters-7M will automatically deprecate its previous version. We will only support and maintain the latest version at all times. Deprecated versions will remain accessible on the dataset website for a few weeks, after which they will be removed. We decided to deprecate old versions to ensure that any data that is requested to be removed (Q32) will be no longer accessible in future versions.

Q58. If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to other users? If so, please provide a description. -Anyone can extend VidChapters-7M by using our data collection code (linked on our website [START_REF]VidChapters-7M project webpage[END_REF]). We are open to accepting extensions via personal communication with contributors. Otherwise, our code and data licenses allow others to create independent derivative works (with proper attribution) as long as they are used for non-commercial academic research.