HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
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Learning text-video embeddings usually requires a dataset of video clips with manually provided captions. However, such datasets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations. The contributions of this work are three-fold. First, we introduce HowTo100M: a large-scale dataset of 136 million video clips sourced from 1.22M narrated instructional web videos depicting humans performing and describing over 23k different visual tasks. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Second, we demonstrate that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask. Finally, we show that this embedding transfers well to other domains: fine-tuning on generic Youtube videos (MSR-VTT dataset) and movies (LSMDC dataset) outperforms models trained on these datasets alone. Our dataset, code and models are publicly available.
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The beginning and end of videos, especially tutorial videos, often consist of an intro, respectively an outro, and these sections are not as related to the content of the videos and contain fewer movements and actions.
“The beginning and end of videos, especially tutorial videos (Miech et al., 2019), often consist of an intro, respectively an outro. These sections are not as related to the content of the videos and contain fewer movements and actions.”
From Intuitive physics understanding emerges from self-supervised pretraining on natural videos (2025) · cites this paper as Miech et al., 2019
Section 3.2 reports that most sampled videos were instructional and that some narration is unrelated to the video. It does not say that beginnings and endings commonly contain intros and outros, or that those sections contain fewer movements and actions. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredMore modalities are emerging beyond vision, text, and audio.
“More modalities.In addition to the general modalities of vision, text, and audio, further diverse modalities are emerging, e.g., Pano-AVQA [144] -the first large-scale spatial and audio-visual question answering dataset on 360 • videos, YouTube-360 (YT-360) [145] (360 • videos), AIST++ [146] (a new multimodal dataset of 3D dance motion and music), Artemis [147] (affective language for visual arts)”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [144]
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Cross Modal Retrieval with Querybank Normalisation
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Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
Wang, T., Li, F., Zhu, L., Li, J., Zhang, Z., & Shen, H. T. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3525147 · Cited by 134 · see citing works
Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning
Amrani, E., Ben‐Ari, R., Rotman, D., & Bronstein, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16822 · Cited by 29 · see citing works
Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers
Hendricks, L. A., Mellor, J., Schneider, R., Alayrac, J.-B., & Nematzadeh, A. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00385 · Cited by 100 · see citing works
Self-citationVideo Graph Transformer for Video Question Answering
Xiao, J., Zhou, P., Chua, T., & Yan, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_3 · Cited by 99 · see citing works
EPIC-KITCHENS-100Preprint
Damen, D., Doughty, H., Farinella, G. M., Furnari, A., Kazakos, E., Ma, J., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13256 · Cited by 111 · see citing works
MIST : Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering
Gao, D., Zhou, L., Ji, L., Zhu, L., Yang, Y., & Shou, M. Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01419 · Cited by 96 · see citing works
Omni-Sourced Webly-Supervised Learning for Video Recognition
Duan, H., Zhao, Y., Xiong, Y., Liu, W., & Lin, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_40 · Cited by 97 · see citing works
Long Movie Clip Classification with State-Space Video Models
Islam, M. M., & Bertasius, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_6 · Cited by 95 · see citing works
VIOLET : End-to-End Video-Language Transformers with Masked Visual-token ModelingPreprint
Fu, T.-J., Li, L., Gan, Z., Lin, K., Wang, W. Y., Wang, L., & Liu, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.12681 · Cited by 130 · see citing works
- Authors
- Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 2630-2640
- OpenAlex
- W2948859046
- Cited by
- 986
- References
- 69
- Citations by year
- 2026: 77 · 2025: 138 · 2024: 170 · 2023: 213 · 2022: 161 · 2021: 155 · 2020: 65 · 2019: 7
- FWCI
- 31.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Funders
- Canadian Institute for Advanced Research
- European Regional Development Fund (02.1.01/0.0/0.0, CZ.02, CZ.02.1.01, CZ.02.1.01/0.0, CZ.02.1.01/0.0/0.0)
- FP7 Ideas: European Research Council (336845)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- video-text retrieval, action localization, domain transfer, large-scale video datasets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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