NEAT adapts VLMs to negation by tuning only text-encoder normalization layers with three losses: entropy refinement, reversed-caption contrastive learning, and textual debiasing.
Leveraging vision-language models for improving domain generalization in image classification
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Negation-Aware Test-Time Adaptation for Vision-Language Models
NEAT adapts VLMs to negation by tuning only text-encoder normalization layers with three losses: entropy refinement, reversed-caption contrastive learning, and textual debiasing.