Using cosine similarity in CLIP embedding space, the paper finds that male and female face sets are differentially associated with occupation and activity statements across all four tested models.
Learning transferable visual models from natural language supervision
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Vision-Language Models display a strong gender bias
Using cosine similarity in CLIP embedding space, the paper finds that male and female face sets are differentially associated with occupation and activity statements across all four tested models.