Papers › Rethinking Generalization in Few-Shot Classification
Rethinking Generalization in Few-Shot Classification
Markus Hiller, Rongkai Ma, Mehrtash Harandi, Tom Drummond
Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be acceptable in many classification scenarios, it poses a significant challenge for applications where the set of classes differs significantly between training and test time. In this paper, we take a closer look at the implications in the context of few-shot learning. Splitting the input samples into patches and encoding these via the help of Vision Transformers allows us to establish semantic correspondences between local regions across images and independent of their respective class. The most informative patch embeddings for the task at hand are then determined as a function of the support set via online optimization at inference time, additionally providing visual interpretability of `what matters most' in the image. We build on recent advances in unsupervised training of networks via masked image modelling to overcome the lack of fine-grained labels and learn the more general statistical structure of the data while avoiding negative image-level annotation influence, aka supervision collapse. Experimental results show the competitiveness of our approach, achieving new state-of-the-art results on four popular few-shot classification benchmarks for $5$-shot and $1$-shot scenarios.
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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 Image Classification | CIFAR-FS 5-way (1-shot) | FewTURE | Accuracy | 77.76 | #17 of 38 | Archive leaderboard | report |
| Few-Shot Image Classification | CIFAR-FS 5-way (5-shot) | FewTURE | Accuracy | 88.90 | #18 of 39 | Archive leaderboard | report |
| Few-Shot Image Classification | FC100 5-way (1-shot) | FewTURE | Accuracy | 47.68 | #11 of 22 | Archive leaderboard | report |
| Few-Shot Image Classification | FC100 5-way (5-shot) | FewTURE | Accuracy | 63.81 | #12 of 22 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | FewTURE | Accuracy | 72.40 | #27 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | FewTURE | Accuracy | 86.38 | #21 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | FewTURE | Accuracy | 76.32 | #18 of 49 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | FewTURE | Accuracy | 89.96 | #7 of 51 | 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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