Papers › Towards Universal Representation for Unseen Action Recognition
Towards Universal Representation for Unseen Action Recognition
Yi Zhu, Yang Long, Yu Guan, Shawn Newsam, Ling Shao
Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper proposes a pipeline using a large-scale training source to achieve a Universal Representation (UR) that can generalise to a more realistic Cross-Dataset UAR (CD-UAR) scenario. We first address UAR as a Generalised Multiple-Instance Learning (GMIL) problem and discover 'building-blocks' from the large-scale ActivityNet dataset using distribution kernels. Essential visual and semantic components are preserved in a shared space to achieve the UR that can efficiently generalise to new datasets. Predicted UR exemplars can be improved by a simple semantic adaptation, and then an unseen action can be directly recognised using UR during the test. Without further training, extensive experiments manifest significant improvements over the UCF101 and HMDB51 benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | ActivityNet | CD-UAR | mAP | 53.8 | #15 of 16 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | CD-UAR | Average accuracy of 3 splits | 51.8 | #74 of 77 | Archive leaderboard | report |
| Action Recognition | UCF101 | CD-UAR | 3-fold Accuracy | 42.5 | #87 of 91 | Archive leaderboard | report |
| Zero-Shot Action Recognition | HMDB51 | UR | Top-1 Accuracy | 24.4 | #21 of 29 | Archive leaderboard | report |
| Zero-Shot Action Recognition | UCF101 | UR | Top-1 Accuracy | 17.5 | #26 of 35 | 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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