Revealing the Gap in Human and VLM Scene Description through Counterfactual Semantic Saliency

Abstract:Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Traditional white-box interpretability methods are inapplicable to closed-source architectures and passive metrics fail to isolate causal features. We introduce Counterfactual Semantic Saliency (CSS). This black-box, model-agnostic framework quantifies the importance of objects by measuring the semantic shift induced by their causal ablation from a scene. To evaluate AI-human semantic alignment, we tested prominent VLMs against a human psychophysics baseline comprising 16,603 valid responses across 307 complex natural scenes and 1,306 high-fidelity counterfactual variants. Our analysis reveals a pervasive scene comprehension gap: models exhibit an overreliance (relative to humans) on large objects (size bias), objects at the center of the image (center bias), and high-saliency objects. In contrast, models rely less on people in the scenes than our human participants do to describe the images. A model's size bias is a primary driver explaining variations in model-human semantic divergence. Code and data are available at this https URL .

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2605.13047 [cs.CV]

(or arXiv:2605.13047v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.13047 Focus to learn more arXiv-issued DOI via DataCite

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