A Closer Look at Spatiotemporal Convolutions for Action Recognition
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.
PDF Abstract CVPR 2018 PDF CVPR 2018 AbstractCode
Colab
UCF101
Kinetics
HMDB51
Sports-1M
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Action Recognition | HMDB-51 | R[2+1]D-TwoStream (Kinetics pretrained) | Average accuracy of 3 splits | 78.7 | # 22 | ||
| Action Recognition | HMDB-51 | R[2+1]D-Flow (Kinetics pretrained) | Average accuracy of 3 splits | 76.4 | # 31 | ||
| Action Recognition | HMDB-51 | R[2+1D]D-TwoStream (Sports1M pretrained) | Average accuracy of 3 splits | 72.7 | # 41 | ||
| Action Recognition | HMDB-51 | R[2+1]D-Flow (Sports1M pretrained) | Average accuracy of 3 splits | 70.1 | # 51 | ||
| Action Recognition | HMDB-51 | R[2+1]D-RGB (Sports1M pretrained) | Average accuracy of 3 splits | 66.6 | # 54 | ||
| Action Classification | Kinetics-400 | R[2+1]D-RGB (Sports-1M pretrain) | Vid acc@1 | 74.3 | # 102 | ||
| Vid acc@5 | 91.4 | # 75 | |||||
| Action Classification | Kinetics-400 | R[2+1]D-Two-Stream | Vid acc@1 | 73.9 | # 103 | ||
| Vid acc@5 | 90.9 | # 79 | |||||
| Action Classification | Kinetics-400 | R[2+1]D-Flow | Vid acc@1 | 67.5 | # 119 | ||
| Vid acc@5 | 87.2 | # 89 | |||||
| Action Recognition | Sports-1M | R[2+1]D-Flow-32frame | Clip Hit@1 | 46.4 | # 3 | ||
| Video hit@1 | 68.4 | # 7 | |||||
| Video hit@5 | 88.7 | # 7 | |||||
| Action Recognition | UCF101 | R[2+1]D-Flow (Kinetics pretrained) | 3-fold Accuracy | 95.5 | # 37 | ||
| Action Recognition | UCF101 | R[2+1]D-TwoStream (Kinetics pretrained) | 3-fold Accuracy | 97.3 | # 14 | ||
| Action Recognition | UCF101 | R[2+1]D-RGB (Sports-1M pretrained) | 3-fold Accuracy | 93.6 | # 51 | ||
| Action Recognition | UCF101 | R[2+1]D-Flow (Sports-1M pretrained) | 3-fold Accuracy | 93.3 | # 53 | ||
| Action Recognition | UCF101 | R[2+1]D-TwoStream (Sports-1M pretrained) | 3-fold Accuracy | 95 | # 42 |
