Scaling Test-Time Robustness of Vision-Language Models via Self-Critical Inference Framework
The emergence of Large Language Models (LLMs) has driven rapid progress in multi-modal learning, particularly in the development of Large Vision-Language Models (LVLMs). However, existing LVLM training paradigms place excessive reliance on the LLM component, giving rise to two critical robustness challenges: language bias and language sensitivity. To address both issues simultaneously, we propose a novel Self-Critical Inference (SCI) framework that extends Visual Contrastive Decoding by conducting multi-round counterfactual reasoning through both textual and visual perturbations. This process further introduces a new strategy for improving robustness by scaling the number of counterfactua...
| Authors | Kaihua Tang, Jiaxin Qi, Jinli Ou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 March 2026 |
| Last updated at source | 25 March 2026 |
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