AI RESEARCH
Eyes on VLM: Benchmarking Gaze Following and Social Gaze Prediction in Vision Language Models
arXiv CS.CV
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ArXi:2605.19859v1 Announce Type: new Vision-language models (VLMs) have rapidly evolved into general-purpose multimodal reasoners with strong zero-shot generalization. In this context, VLMs could greatly benefit the analysis of human gaze and attention, a central task in human behavior understanding that requires reasoning about the physical scene as well as the activity, interactions, and social context. However, the extent to which VLMs can reliably understand human gaze and related attentional behaviors remains largely unexplored.