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
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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Cites
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Learning Transferable Visual Models From Natural Language Supervision
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Multimodal learning with transformers: A survey
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ImageBind One Embedding Space to Bind Them All
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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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