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Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing methods overlook data characteristics and simply reuse the training strategies adopted during pre-training. Specifically, they treat same-class samples as distinct instances and transform images independently of their paired text prompts, which makes model learning more difficult. We address these limitations through transformation-aware prompt conditioning and a re-calibrated contrastive loss. Fixed text descriptors identify the transformatio

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