Papers › Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Yuqian Fu, Li Zhang, Junke Wang, Yanwei Fu, Yu-Gang Jiang
Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This observation has motivated an increasing interest in few-shot video action recognition, which aims at learning new actions with only very few labeled samples. In this paper, we propose a depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net. Concretely, we tackle the few-shot recognition problem from three aspects: firstly, we alleviate this extremely data-scarce problem by introducing depth information as a carrier of the scene, which will bring extra visual information to our model; secondly, we fuse the representation of original RGB clips with multiple non-strictly corresponding depth clips sampled by our temporal asynchronization augmentation mechanism, which synthesizes new instances at feature-level; thirdly, a novel Depth Guided Adaptive Instance Normalization (DGAdaIN) fusion module is proposed to fuse the two-stream modalities efficiently. Additionally, to better mimic the few-shot recognition process, our model is trained in the meta-learning way. Extensive experiments on several action recognition benchmarks demonstrate the effectiveness of our model.
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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 |
|---|---|---|---|---|---|---|---|
| Few Shot Action Recognition | HMDB51 | AMeFu-Net | 1:1 Accuracy | 75.5 | #5 of 7 | Archive leaderboard | report |
| Few Shot Action Recognition | Kinetics-100 | AMeFu-Net | Accuracy | 86.8 | #2 of 8 | Archive leaderboard | report |
| Few Shot Action Recognition | UCF101 | AMeFu-Net | 1:1 Accuracy | 95.5 | #4 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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