Meta-Contrastive Learning for Vision-Language Models via Task-Adaptive CLIP Training
We propose Domain-Conditioned Meta-Contrastive Learning, a framework for improving the cross-domain generalization of vision-language models. While contrastive models such as CLIP achieve strong performance through large-scale training, they rely on a global objective that does not explicitly account for domain shift. To address this limitation, we formulate multimodal learning as a bilevel meta-learning problem over domain-conditioned tasks. Specifically, we introduce domain embeddings that modulate image and text representations, and optimize the model for rapid adaptation to domain-specific distributions via gradient-based inner-loop updates. In addition, we incorporate a cross-domain alignment regularization to encourage domain-invariant representations. Our approach is compatible with standard contrastive training pipelines and can be applied to heterogeneous datasets spanning natural and medical domains. We expect improved robustness under domain shift and enhanced few-shot adaptation performance, highlighting a promising direction for scalable multimodal learning.