Papers with image-to-image

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A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models (2024.findings-emnlp)

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Challenge: Existing studies have highlighted the existence of social biases within large vision and language models.
Approach: They propose a framework for systematically evaluating gender, race, and age biases in vision-language models with respect to professions.
Outcome: The proposed framework covers all supported inference modes of the recent vision-language models, including image-to-text, text-to image, and image- to-image.

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