Papers › FASTER Recurrent Networks for Efficient Video Classification
FASTER Recurrent Networks for Efficient Video Classification
Linchao Zhu, Laura Sevilla-Lara, Du Tran, Matt Feiszli, Yi Yang, Heng Wang
Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visually similar clips independently ignores the temporal structure of the video sequence, and increases the computational cost at inference time. In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities. The FASTER framework can integrate high quality representations from expensive models to capture subtle motion information and lightweight representations from cheap models to cover scene changes in the video. A new recurrent network (i.e., FAST-GRU) is designed to aggregate the mixture of different representations. Compared with existing approaches, FASTER can reduce the FLOPs by over 10x? while maintaining the state-of-the-art accuracy across popular datasets, such as Kinetics, UCF-101 and HMDB-51.
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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 | Kinetics-400 | FASTER32 | Acc@1 | 75.1 | #157 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | FASTER16 w/o sp | Acc@1 | 71.7 | #177 of 207 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | FASTER32 (Kinetics pretrain) | Average accuracy of 3 splits | 75.7 | #38 of 77 | Archive leaderboard | report |
| Action Recognition | UCF101 | FASTER32 | 3-fold Accuracy | 96.9 | #29 of 91 | 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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