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Action Recognition In Videos On Sports 1M
Metrics
Video hit@1
Video hit@5
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| ip-CSN-152 (RGB) | 75.5 | 92.8 | Video Classification with Channel-Separated Convolutional Networks | |
| ip-CSN-101 (RGB) | 74.9 | 92.6 | Video Classification with Channel-Separated Convolutional Networks | |
| R[2+1]D-Two-Stream-32frame | 73.3 | 91.9 | A Closer Look at Spatiotemporal Convolutions for Action Recognition | |
| R[2+1]D-RGB-32frame | 73 | 91.5 | A Closer Look at Spatiotemporal Convolutions for Action Recognition | |
| Conv pooling | 71.7 | 90.4 | Beyond Short Snippets: Deep Networks for Video Classification | |
| R[2+1]D-Flow-32frame | 68.4 | 88.7 | A Closer Look at Spatiotemporal Convolutions for Action Recognition | |
| P3D | 66.4 | 87.4 | Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks | |
| C3D | 61.1 | 85.5 | Learning Spatiotemporal Features with 3D Convolutional Networks | |
| DeepVideo’s Slow Fusion | 60.9 | 80.2 | Large-Scale Video Classification with Convolutional Neural Networks | - |
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