Papers › Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
Shuo Li, Fang Liu, Zehua Hao, Kaibo Zhao, Licheng Jia
Most few-shot image classification methods are trained based on tasks. Usually, tasks are built on base classes with a large number of labeled images, which consumes large effort. Unsupervised few-shot image classification methods do not need labeled images, because they require tasks to be built on unlabeled images. In order to efficiently build tasks with unlabeled images, we propose a novel single-stage clustering method: Learning Features into Clustering Space (LF2CS), which first set a separable clustering space by fixing the clustering centers and then use a learnable model to learn features into the clustering space. Based on our LF2CS, we put forward an image sampling and c-way k-shot task building method. With this, we propose a novel unsupervised few-shot image classification method, which jointly learns the learnable model, clustering and few-shot image classification. Experiments and visualization show that our LF2CS has a strong ability to generalize to the novel categories. From the perspective of image sampling, we implement four baselines according to how to build tasks. We conduct experiments on the Omniglot, miniImageNet, tieredImageNet and CIFARFS datasets based on the Conv-4 and ResNet-12 backbones. Experimental results show that ours outperform the state-of-the-art methods.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | LF2CS | Accuracy | 53.14 | #12 of 28 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | LF2CS | Accuracy | 67.36 | #13 of 28 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | LF2CS | Accuracy | 53.16 | #6 of 12 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | LF2CS | Accuracy | 66.59 | #6 of 12 | 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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