S$^2$COPE: Self-Supervised Concept Discovery via Preference Learning
Self-supervised learning can learn useful representations from unlabeled images. Yet these representations remain largely opaque, making it difficult to understand what visual concepts they encode. Can self-supervision instead produce useful visual concepts that are directly understandable to people? We introduce S$^2$COPE, a framework that represents images as sets of concepts expressed in natural language. A vision-language model proposes candidate concepts, and a self-supervised objective evaluates how well each concept describes the source image while distinguishing it from other images. This feedback is used to construct preferences and train the model to propose better concepts. Acr...
| Authors | Shilong Xiang, Zirui Zhang, Chengzhi Mao |
|---|---|
| Primary category | cs.CV |
| Published | 12 June 2026 |
| Last updated at source | 27 September 2026 |
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