Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning
Vision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language learning mitigates this limitation by leveraging a small set of labeled image-text pairs together with abundant unlabeled images, existing methods remain fundamentally pairwise and fail to model the global structure of multimodal representation manifolds. Most existing topology-based alignment methods rely on persistence diagram matching, which neither guarantees geometric alignment nor uses the image-text pairing information central to vision-language learning. We propose Topology-Aware Representation Alignment for Semi-Supervised Vision-Lan...
| Authors | Junwon You, Mihyun Jang, Sangwoo Mo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 14 October 2026 |
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