CoDA: Co-Adaptive Dual-Path Alignment for Vision-Language Models
In CoDA, a new adaptation framework that explicitly disentangles and coordinates cross-modal semantic alignment and intra-modal structural consistency is proposed, and it is shown that CoDA outperforms state-of-the-art parameter-efficient methods, particularly under few-shot learning and distribution-shift scenarios.
Yi Zhang, Rui Zhu, Channi Li et al.
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