AI RESEARCH
LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation
arXiv CS.CV
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ArXi:2605.17287v1 Announce Type: new Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems. Beyond being vulnerable to sudden lighting changes and sensor noise, spatial-domain models struggle to disentangle authentic gaze cues from irrelevant visual attributes. In this paper, we propose LISA, a \textbf{L}anguage-guided \textbf{I}nterference-aware \textbf{S}patial-Frequency \textbf{A}ttention framework that combines frequency-domain priors with vision-language knowledge.