Papers › Temporal Convolutional Networks for Action Segmentation and Detection
Temporal Convolutional Networks for Action Segmentation and Detection
Colin Lea, Michael D. Flynn, Rene Vidal, Austin Reiter, Gregory D. Hager
The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that captures high-level temporal patterns. We introduce a new class of temporal models, which we call Temporal Convolutional Networks (TCNs), that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling to efficiently capture long-range temporal patterns whereas our Dilated TCN uses dilated convolutions. We show that TCNs are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks. We apply these models to three challenging fine-grained datasets and show large improvements over the state of the art.
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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 Segmentation | GTEA | ED-TCN | Acc | 64.0 | #27 of 28 | Archive leaderboard | report |
| Action Segmentation | GTEA | ED-TCN | Edit | - | #27 of 28 | Archive leaderboard | report |
| Action Segmentation | GTEA | ED-TCN | F1@10% | 72.2 | #27 of 28 | Archive leaderboard | report |
| Action Segmentation | GTEA | ED-TCN | F1@25% | 69.3 | #27 of 28 | Archive leaderboard | report |
| Action Segmentation | GTEA | ED-TCN | F1@50% | 56.0 | #27 of 28 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | TCN | Accuracy (AV I) | 43% | #6 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | TCN | Accuracy (AV II) | 64% | #6 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | TCN | Accuracy (CS) | 56% | #6 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | TCN | Accuracy (CV I) | 16% | #6 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Varying-view RGB-D Action-Skeleton | TCN | Accuracy (CV II) | 43% | #6 of 7 | 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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