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Ioannis Sarridis

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Preprint Jul 2026

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

This is the first large-scale empirical study of 194 publicly available VLMs, including 16 model families, covering a wide range of model sizes, 24 training datasets, and three evaluation benchmarks, namely ImageNet (overall performance), CelebA (typical single-attribute bias), and UrbanCars (complex multi-attribute biases).

Ioannis Sarridis, Ioannis Kompatsiaris, Symeon Papadopoulos · 0 citations