Papers › Long-Term Feature Banks for Detailed Video Understanding
Long-Term Feature Banks for Detailed Video Understanding
Chao-yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krähenbühl, Ross Girshick
To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank---supportive information extracted over the entire span of a video---to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Classification | Charades | LFB | MAP | 42.5 | #25 of 49 | Archive leaderboard | report |
| Action Recognition | AVA v2.1 | LFB (Kinetics-400 pretraining) | mAP (Val) | 27.7 | #5 of 15 | Archive leaderboard | report |
| Egocentric Activity Recognition | EPIC-KITCHENS-55 | LFB Max | Actions Top-1 (S1) | 32.70 | #4 of 7 | Archive leaderboard | report |
| Egocentric Activity Recognition | EPIC-KITCHENS-55 | LFB Max | Actions Top-1 (S2) | 21.2 | #4 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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