AI RESEARCH
Multi-Modal Representation Learning via Semi-Supervised Rate Reduction for Generalized Category Discovery
arXiv CS.CV
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ArXi:2602.19910v2 Announce Type: replace Generalized Category Discovery (GCD) aims to identify both known and unknown categories, with only partial labels given for the known categories, posing a challenging open-set recognition problem. State-of-the-art approaches for GCD task are usually built on multi-modality representation learning, which is heavily dependent upon inter-modality alignment. However, few of them cast a proper intra-modality alignment to generate a desired underlying structure of representation distributions.