Title: GroundAttack: Mitigating Easy-Options Bias for Visual Question Answering
Primary area: datasets and benchmarks
Keywords: Visual Question Answering, Shortcut learning, Vision Languame Models

Abstract: In this early study, we observe an Easy-Opitions Bias (EOB) issue in several multiple-choice Visual Question Answering (VQA) benchmarks, including MMStar, RealWorldQA, SEED-Bench, NeXT-QA, STAR benchmark, and Video-MME. This bias allows vision-language models (VLMs) to select the correct answer using only the vision ($\boldsymbol{V}$) and options ($\boldsymbol{O}$) as inputs, without the need for the question ($\boldsymbol{Q}$). Through grounding experiments, we attribute the bias to an imbalance in visual relevance: the correct answer typically aligns more closely with the visual contents than the negative options in feature space, creating a shortcut for VLMs to infer the answer via simply vision-option similarity matching. To mitigate this, we introduce GroundAttack, an agentical method that automatically generates hard negative options as visually plausible as the correct answer. 
We apply it to the NeXT-QA and MMStar datasets, creating new EOB-free annotations. On these EOB-free annotations, current VLMs approach random accuracies under ($\boldsymbol{V}$+$\boldsymbol{O}$) settings, and drop to non-saturated accuracies under ($\boldsymbol{V}$+$\boldsymbol{Q}$+$\boldsymbol{O}$) settings, providing a more realistic evaluation of VLMs' QA ability.

TL;DR: We reveal and address an Easy-Options Bias in VQA benchmarks by introducing GroundAttack, which generates hard negative options to enable fairer evaluation of vision-language models.