LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation

• URL: http://arxiv.org/abs/2605.17287v1
• Date: Sun, 17 May 2026 06:57:31 GMT
• Title: LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation
• Abstract summary: Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems.<n>We propose LISA, a textbfLanguage-guided textbfInterference-aware textbfSpatial-Frequency textbfAttention framework.
• Score: 12.034295331199813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Observing that the amplitude spectrum remains relatively stable even under spatial perturbations, we design a dual-domain fusion mechanism. It integrates stable low-frequency semantics into high-frequency details, employing spatial attention to precisely target ocular regions. To reduce semantic ambiguity, we also introduce a training-time disentanglement strategy. Using a frozen CLIP encoder and orthogonal regularization, we explicitly separate gaze features from appearance interference. Experiments on two benchmarks show that LISA achieves state-of-the-art performance, with significantly improved robustness against occlusions and lighting variations. The code repository is available at https://github.com/Mason-bupt/LISA.

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