{"type":"ScholarlyArticle","title":"LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation","authors":[{"name":"Jun Ma","slug":"jun-ma","given":"Jun","family":"Ma","affiliation":""},{"name":"Zhenye Yang","slug":"zhenye-yang","given":"Zhenye","family":"Yang","affiliation":""},{"name":"Ruichen Zhou","slug":"ruichen-zhou","given":"Ruichen","family":"Zhou","affiliation":""},{"name":"Pei Zhang","slug":"pei-zhang","given":"Pei","family":"Zhang","affiliation":""},{"name":"Huan Li","slug":"huan-li","given":"Huan","family":"Li","affiliation":""},{"name":"Jinpeng Chen","slug":"jinpeng-chen","given":"Jinpeng","family":"Chen","affiliation":""}],"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 Language-guided Interference-aware Spatial-Frequency Attention 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.","doi":"10.24963/ijcai.2026/325","url":"https://ijcai.org/proceedings/2026/325","pdf":"https://ijcai.org/proceedings/2026/0325.pdf","markdown":"https://ijcai.org/proceedings/2026/325.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/325","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":325,"pages":{"first":2924,"last":2932},"keywords":["Data Mining: Applications","Machine Learning: Multi-modal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}