Proceedings

CoDA: Co-adaptive Dual-path Alignment for Vision-Language Models

Yi Zhang, Rui Zhu, Channi Li, Xiaoxu Li, Zhanyu Ma, Jing-Hao Xue

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 2100-2108.

https://doi.org/10.24963/ijcai.2026/234

Keywords

  • Machine Learning: Few-shot learning
  • Computer Vision: Transfer, low-shot, semi- and un- supervised learning
  • Computer Vision: Multimodal learning

Abstract

Parameter-efficient adaptation methods, such as adapters and prompt learning, have become popular for transferring pre-trained vision-language models (VLMs) to downstream tasks. However, under limited supervision during adaptation, overly aggressive cross-modal alignment can distort the intrinsic structure of modality-specific representations, leading to degraded generalization. In this paper, we propose Co-adaptive Dual-path Alignment (CoDA), a new adaptation framework that explicitly disentangles and coordinates cross-modal semantic alignment and intra-modal structural consistency. In CoDA, we also propose a parent class labeling strategy that injects hierarchical semantic priors into textual prompts, further stabilizing alignment under limited supervision. Extensive experiments on eleven benchmark datasets show that CoDA outperforms state-of-the-art parameter-efficient methods, particularly under few-shot learning and distribution-shift scenarios. Our code is available at https://github.com/yizhang-ac/CoDA.

Yi Zhang, Rui Zhu, Channi Li, Xiaoxu Li, Zhanyu Ma, and Jing-Hao Xue. 2026. CoDA: Co-adaptive Dual-path Alignment for Vision-Language Models. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 2100–2108. https://doi.org/10.24963/ijcai.2026/234
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