Papers › Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Lionel Pigou, Aäron van den Oord, Sander Dieleman, Mieke Van Herreweghe, Joni Dambre
Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition. For the task of capturing temporal structure in video, however, there still remain numerous open research questions. Current research suggests using a simple temporal feature pooling strategy to take into account the temporal aspect of video. We demonstrate that this method is not sufficient for gesture recognition, where temporal information is more discriminative compared to general video classification tasks. We explore deep architectures for gesture recognition in video and propose a new end-to-end trainable neural network architecture incorporating temporal convolutions and bidirectional recurrence. Our main contributions are twofold; first, we show that recurrence is crucial for this task; second, we show that adding temporal convolutions leads to significant improvements. We evaluate the different approaches on the Montalbano gesture recognition dataset, where we achieve state-of-the-art results.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Gesture Recognition | Montalbano | Temp Conv + LSTM | Error rate | 2.77 | #1 of 1 | Archive leaderboard | report |
| Gesture Recognition | Montalbano | Temp Conv + LSTM | Jaccard (Mean) | 90.6 | #1 of 1 | Archive leaderboard | report |
| Gesture Recognition | Montalbano | Temp Conv + LSTM | Precision | 94.49 | #1 of 1 | Archive leaderboard | report |
| Gesture Recognition | Montalbano | Temp Conv + LSTM | Recall | 94.57 | #1 of 1 | 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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