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Broaden Your Views for Self-Supervised Video Learning — arXiv:2103.16559

Source: arXiv
Original: https://arxiv.org/abs/2103.16559
License (as archived): CC0-1.0
Archived: 2026-10-06T05:31:50.298Z
SHA-256 of the archived text: 9400c7a89377ea2e61f028bd8d2eea0f4aef2c460a49f87f0d45eb57ea4a0daa
Stable URL of this page: https://metraipolis.com/documento/dc_04baedbce5cd

How to cite

Broaden Your Views for Self-Supervised Video Learning — arXiv:2103.16559 — arXiv, https://arxiv.org/abs/2103.16559 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_04baedbce5cd (Metraipolis archive dc_04baedbce5cd, sha256 9400c7a89377ea2e61f028bd8d2eea0f4aef2c460a49f87f0d45eb57ea4a0daa)

BibTeX

@misc{dc04baedbce5cd,
  title = {Broaden Your Views for Self-Supervised Video Learning — arXiv:2103.16559},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2103.16559},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_04baedbce5cd; sha256 9400c7a89377ea2e61f028bd8d2eea0f4aef2c460a49f87f0d45eb57ea4a0daa},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2103.16559. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a crucial element in the video domain: time. We introduce BraVe, a self-supervised learning framework for video. In BraVe, one of the views has access to a narrow temporal window of the video while the other view has a broad access to the video content. Our models learn to generalise from the narrow view to the general content of the video. Furthermore, BraVe processes the views with different backbones, enabling the use of alternative augmentations or modalities into the broad view such as optical flow, randomly convolved RGB frames, audio or their combinations. We demonstrate that BraVe achieves state-of-the-art results in self-supervised representation learning on standard video and audio classification benchmarks including UCF101, HMDB51, Kinetics, ESC-50 and AudioSet.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2103.16559 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:27:05.918Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).