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Cited by 580

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

End-to-End Learning of Visual Representations From Uncurated Instructional Videos

Miech, Antoine, Alayrac, Jean-Baptiste, Smaira, Lucas, Laptev, Ivan, Sivic, Josef, Zisserman, Andrew·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines.

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Metadata

Authors
Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Sivic, J., & Zisserman, A.
Venue
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2020-06
Pages
9876-9886
ISSN
1063-6919 (ISSN-L)
OpenAlex
W3035635319
Cited by
580
References
17
FWCI
33.74
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: End-to-End Learning of Visual Representations From Uncurated Instructional Videos. https://app.cochat.ai/papers/10.1109/cvpr42600.2020.00990
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Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Sivic, J., & Zisserman, A. (2020). End-to-End Learning of Visual Representations From Uncurated Instructional Videos. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00990

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