Papers › A Closer Look at Spatiotemporal Convolutions for Action Recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann Lecun, Manohar Paluri
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. In this work we empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within the framework of residual learning. Furthermore, we show that factorizing the 3D convolutional filters into separate spatial and temporal components yields significantly advantages in accuracy. Our empirical study leads to the design of a new spatiotemporal convolutional block "R(2+1)D" which gives rise to CNNs that achieve results comparable or superior to the state-of-the-art on Sports-1M, Kinetics, UCF101 and HMDB51.
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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 | Kinetics-400 | R[2+1]D-Flow (Sports-1M pretrain) | Acc@1 | 75.4 | #156 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-Flow (Sports-1M pretrain) | Acc@5 | 91.9 | #156 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-RGB (Sports-1M pretrain) | Acc@1 | 74.3 | #163 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-RGB (Sports-1M pretrain) | Acc@5 | 91.4 | #163 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-Two-Stream | Acc@1 | 73.9 | #165 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-Two-Stream | Acc@5 | 90.9 | #165 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D | Acc@1 | 72 | #175 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D | Acc@5 | 90 | #175 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-RGB | Acc@1 | 72 | #176 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-RGB | Acc@5 | 90 | #176 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-Flow | Acc@1 | 67.5 | #185 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | R[2+1]D-Flow | Acc@5 | 87.2 | #185 of 207 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | R[2+1]D-TwoStream (Kinetics pretrained) | Average accuracy of 3 splits | 78.7 | #28 of 77 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | R[2+1]D-Flow (Kinetics pretrained) | Average accuracy of 3 splits | 76.4 | #36 of 77 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | R[2+1]D-RGB (Kinetics pretrained) | Average accuracy of 3 splits | 74.5 | #41 of 77 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | R[2+1D]D-TwoStream (Sports1M pretrained) | Average accuracy of 3 splits | 72.7 | #46 of 77 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | R[2+1]D-Flow (Sports1M pretrained) | Average accuracy of 3 splits | 70.1 | #55 of 77 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | R[2+1]D-RGB (Sports1M pretrained) | Average accuracy of 3 splits | 66.6 | #59 of 77 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-Two-Stream-32frame | Video hit@1 | 73.3 | #3 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-Two-Stream-32frame | Video hit@5 | 91.9 | #3 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-RGB-32frame | Clip Hit@1 | 57 | #4 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-RGB-32frame | Video hit@1 | 73 | #4 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-RGB-32frame | Video hit@5 | 91.5 | #4 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-Flow-32frame | Clip Hit@1 | 46.4 | #6 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-Flow-32frame | Video hit@1 | 68.4 | #6 of 9 | Archive leaderboard | report |
| Action Recognition | Sports-1M | R[2+1]D-Flow-32frame | Video hit@5 | 88.7 | #6 of 9 | Archive leaderboard | report |
| Action Recognition | UCF101 | R[2+1]D-TwoStream (Kinetics pretrained) | 3-fold Accuracy | 97.3 | #20 of 91 | Archive leaderboard | report |
| Action Recognition | UCF101 | R[2+1]D-RGB (Kinetics pretrained) | 3-fold Accuracy | 96.8 | #30 of 91 | Archive leaderboard | report |
| Action Recognition | UCF101 | R[2+1]D-Flow (Kinetics pretrained) | 3-fold Accuracy | 95.5 | #44 of 91 | Archive leaderboard | report |
| Action Recognition | UCF101 | R[2+1]D-TwoStream (Sports-1M pretrained) | 3-fold Accuracy | 95 | #49 of 91 | Archive leaderboard | report |
| Action Recognition | UCF101 | R[2+1]D-RGB (Sports-1M pretrained) | 3-fold Accuracy | 93.6 | #59 of 91 | Archive leaderboard | report |
| Action Recognition | UCF101 | R[2+1]D-Flow (Sports-1M pretrained) | 3-fold Accuracy | 93.3 | #61 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.
Methods
Introduced by this paper: (2+1)D Convolution, R(2+1)D
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