Papers › Collaborative Spatiotemporal Feature Learning for Video Action Recognition
Collaborative Spatiotemporal Feature Learning for Video Action Recognition
Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu
Spatiotemporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independently (C2D) or jointly with unconstrained parameters (C3D). In this paper, we propose a novel neural operation which encodes spatiotemporal features collaboratively by imposing a weight-sharing constraint on the learnable parameters. In particular, we perform 2D convolution along three orthogonal views of volumetric video data, which learns spatial appearance and temporal motion cues respectively. By sharing the convolution kernels of different views, spatial and temporal features are collaboratively learned and thus benefit from each other. The complementary features are subsequently fused by a weighted summation whose coefficients are learned end-to-end. Our approach achieves state-of-the-art performance on large-scale benchmarks and won the 1st place in the Moments in Time Challenge 2018. Moreover, based on the learned coefficients of different views, we are able to quantify the contributions of spatial and temporal features. This analysis sheds light on interpretability of the model and may also guide the future design of algorithm for video recognition.
Code
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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 | CoST ResNet-101 (ImageNet pretrain) | Acc@1 | 77.5 | #138 of 207 | Archive leaderboard | report |
| Action Classification | MiT | CoST (ResNet-101, 32 frames) | Top 1 Accuracy | 32.4% | #18 of 29 | Archive leaderboard | report |
| Action Classification | MiT | CoST (ResNet-101, 32 frames) | Top 5 Accuracy | 60.0% | #18 of 29 | 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.
Methods
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