# VidChapters-7M: Video Chapters at Scale

arXiv: https://arxiv.org/abs/2309.13952 · HTML: https://mlsanity.com/paper/2309.13952/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 106 of its 144 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 30 of 106)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)

## Built on by (most cited first, 30 of 39)

- [VideoLLM-online: Online Video Large Language Model for Streaming Video](https://mlsanity.com/paper/2406.11816/) (2024)
- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM](https://mlsanity.com/paper/2403.18406/) (2024)
- [Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning](https://mlsanity.com/paper/2508.04416/) (2025)
- [Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models](https://mlsanity.com/paper/2504.15271/) (2025)
- [LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling](https://mlsanity.com/paper/2511.20785/) (2025)
- [ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos](https://mlsanity.com/paper/2406.19392/) (2024)
- [V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning](https://mlsanity.com/paper/2404.12353/) (2024)
- [Mind the Time: Temporally-Controlled Multi-Event Video Generation](https://mlsanity.com/paper/2412.05263/) (2024)
- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [HowToCaption: Prompting LLMs to Transform Video Annotations at Scale](https://mlsanity.com/paper/2310.04900/) (2023)
- [COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training](https://mlsanity.com/paper/2401.00849/) (2024)
- [TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning](https://mlsanity.com/paper/2404.09275/) (2024)
- [Learning Video Context as Interleaved Multimodal Sequences](https://mlsanity.com/paper/2407.21757/) (2024)
- [Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos](https://mlsanity.com/paper/2312.17117/) (2023)
- [Action100M: A Large-scale Video Action Dataset](https://mlsanity.com/paper/2601.10592/) (2026)
- [Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos](https://mlsanity.com/paper/2311.16444/) (2023)
- [VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning](https://mlsanity.com/paper/2501.06761/) (2025)
- [Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs](https://mlsanity.com/paper/2504.00072/) (2025)
- [LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale](https://mlsanity.com/paper/2504.16030/) (2025)
- [PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters](https://mlsanity.com/paper/2410.16148/) (2024)
- [VLog: Video-Language Models by Generative Retrieval of Narration Vocabulary](https://mlsanity.com/paper/2503.09402/) (2025)
- [From Trial to Triumph: Advancing Long Video Understanding via Visual Context Sample Scaling and Self-reward Alignment](https://mlsanity.com/paper/2503.20472/) (2025)
- [Compass: SLO-aware Query Planner for Compound AI Serving at Scale](https://mlsanity.com/paper/2504.16397/) (2025)
- [TimeViper: A Hybrid Mamba-Transformer Vision-Language Model for Efficient Long Video Understanding](https://mlsanity.com/paper/2511.16595/) (2025)
- [Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality](https://mlsanity.com/paper/2403.19221/) (2024)
- [Beyond Transcripts: A Renewed Perspective on Audio Chaptering](https://mlsanity.com/paper/2602.08979/) (2026)
- [Scene-VLM: Multimodal Video Scene Segmentation via Vision-Language Models](https://mlsanity.com/paper/2512.21778/) (2025)
- [Roles of MLLMs in Visually Rich Document Retrieval for RAG: A Survey](https://mlsanity.com/paper/2601.03262/) (2025)
- [A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video](https://mlsanity.com/paper/2312.01575/) (2023)

## Abstract (arXiv)

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.