Papers › Two-Stream Convolutional Networks for Action Recognition in Videos
Two-Stream Convolutional Networks for Action Recognition in Videos
Karen Simonyan, Andrew Zisserman
We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multi-task learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification.
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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 | Charades | 2-Strm | MAP | 18.6 | #48 of 49 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | Two-Stream (ImageNet pretrained) | Average accuracy of 3 splits | 59.4 | #68 of 77 | Archive leaderboard | report |
| Action Recognition | UCF101 | Two-Stream (ImageNet pretrained) | 3-fold Accuracy | 88.0 | #75 of 91 | Archive leaderboard | report |
| Hand Gesture Recognition | VIVA Hand Gestures Dataset | Two Stream CNNs | Accuracy | 68 | #3 of 3 | 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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