Papers › LSTA: Long Short-Term Attention for Egocentric Action Recognition
LSTA: Long Short-Term Attention for Egocentric Action Recognition
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state of the art performance on four standard benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Egocentric Activity Recognition | EGTEA | LSTA | Average Accuracy | 61.9 | #5 of 6 | Archive leaderboard | report |
| Egocentric Activity Recognition | EGTEA | LSTA | Mean class accuracy | - | #5 of 6 | Archive leaderboard | report |
| Egocentric Activity Recognition | EPIC-KITCHENS-55 | LSTA | Actions Top-1 (S2) | 16.63 | #7 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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