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arXiv · 2501.00569

Probing Visual Language Priors in VLMs

Tiange Luo·Ang Cao·Gunhee Lee·Justin Johnson·Honglak Lee
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Abstract

Despite recent advances in Vision-Language Models (VLMs), they may over-rely on visual language priors existing in their training data rather than true visual reasoning. To investigate this, we introduce ViLP, a benchmark featuring deliberately out-of-distribution images synthesized via image generation models and out-of-distribution Q&A pairs. Each question in ViLP is coupled with three potential answers and three corresponding images: one that can be resolved by text priors alone and two that demand visual reasoning. Although, humans achieve near-perfect accuracy, modern VLMs falter; for instance, GPT-4 achieves only 66.17% on ViLP. To alleviate this, we propose a self-improving framework in which models generate new VQA data, then apply pixel-level and semantic corruptions to form "good-bad" image pairs for self-training. Our training objectives compel VLMs to focus more on the actual visual inputs, and we demonstrate their effectiveness in boosting the performance of open-source VLMs, including LLaVA-v1.5 and Cambrian.

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Tiange Luo, Ang Cao, Gunhee Lee, Justin Johnson, Honglak Lee. 2024-12-31. Probing Visual Language Priors in VLMs. https://arxiv.org/abs/2501.00569

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