Papers › Temporal-Relational CrossTransformers for Few-Shot Action Recognition
Temporal-Relational CrossTransformers for Few-Shot Action Recognition
Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen
We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous few-shot works, we construct class prototypes using the CrossTransformer attention mechanism to observe relevant sub-sequences of all support videos, rather than using class averages or single best matches. Video representations are formed from ordered tuples of varying numbers of frames, which allows sub-sequences of actions at different speeds and temporal offsets to be compared. Our proposed Temporal-Relational CrossTransformers (TRX) achieve state-of-the-art results on few-shot splits of Kinetics, Something-Something V2 (SSv2), HMDB51 and UCF101. Importantly, our method outperforms prior work on SSv2 by a wide margin (12%) due to the its ability to model temporal relations. A detailed ablation showcases the importance of matching to multiple support set videos and learning higher-order relational CrossTransformers.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few Shot Action Recognition | HMDB51 | TRX | 1:1 Accuracy | 75.6 | #4 of 7 | Archive leaderboard | report |
| Few Shot Action Recognition | Kinetics-100 | TRX | Accuracy | 85.9 | #5 of 8 | Archive leaderboard | report |
| Few Shot Action Recognition | Something-Something-100 | TRX | 1:1 Accuracy | 64.6 | #3 of 5 | Archive leaderboard | report |
| Few Shot Action Recognition | UCF101 | TRX | 1:1 Accuracy | 96.1 | #3 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.
Methods
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