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cs.CV2025
Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation
Yusuke Hirota, Ryo Hachiuma, Boyi Li +9
Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-worl…
cs.CV2025
From Global to Local: Social Bias Transfer in CLIP
Ryan Ramos, Yusuke Hirota, Yuta Nakashima +1
The recycling of contrastive language-image pre-trained (CLIP) models as backbones for a large number of downstream tasks calls for a thorough analysis of their transferability imp…
cs.CV2025★ 1 cited
LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Yusuke Hirota, Boyi Li, Ryo Hachiuma +7
Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating de…