Revealing the Gap in Human and VLM Scene Description through Counterfactual Semantic Saliency
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-fid...
| Authors | Ziqi Wen, Parsa Madinei, Miguel P. Eckstein |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 10 October 2026 |
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