Title: SEEING THROUGH LANGUAGE: HOW TEXT REVEALS OBJECT AND STATE BIAS IN VLMS
Primary area: datasets and benchmarks
Keywords: Computer Vision, Vision Language models, Object State Bias, dataset and benchmark, model bias

Abstract: Vision-Language models (VLMs) have demonstrated strong performance across a variety of multimodal benchmarks though not without internal biases. Little is known about how VLMs balance sensitivity to object identity versus object state. In this work, we systematically investigate object-state bias in VLMs by evaluating a broad set of models spanning diverse architectures and sizes. To enable controlled analysis, we introduce the Benchmark for Biases in Objects and States (BBiOS) dataset containing objects in both their original and transformed states. Across a variety of experiments, we examine model performance on recognizing objects, states, and their interactions. Our results reveal a consistent object bias, where models reliably recognize object categories but struggle to accurately capture states. Furthermore, attempts to steer models toward greater state sensitivity through prompting or injecting oracle information yield only marginal improvements. These findings highlight a fundamental limitation in current VLMs, suggesting that different training strategies or architectural innovations are required to reduce object-state bias in multimodal reasoning.