ViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models
Gender bias in large vision-language models (LVLMs) undermines their fairness and reliability, compromising output trustworthiness. Current mitigation methods rely on training-phase adjustments or post-hoc calibration, but face limitations in dynamic visual bias mitigation. These include inability to capture real-time visual-textual incongruence, dependence on predefined gender bias taxonomies, and degraded cross-modal alignment with emergent bias patterns. To address these challenges, we propose ViD, a causally-inspired framework that analyzes attention mechanisms across five distinct patterns, revealing confounding effects from strong language priors. ViD demonstrates that visual-to-lan...
| Authors | Zhipeng Zhao, Zhaoqiang Wei, Peishun Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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