Showing cs.CVShow all
3 papers · 1 filter
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★ 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…
cs.CV2024
From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment
Yusuke Hirota, Ryo Hachiuma, Chao-Han Huck Yang +1
Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. This generative approach to image caption enrichment further makes textual…