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cs.CV2026
CoRE: Weakly Supervised Coarse-to-Fine Risk Evidence Learning in Driving Videos
Kaiser Hamid, Can Cui, Nade Liang
Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level judgments. Learning \emph{wh…
cs.CV2025
FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling
Kaiser Hamid, Can Cui, Khandakar Ashrafi Akbar +2
Understanding not only where drivers look but also why their attention shifts is essential for interpretable human-AI collaboration in autonomous driving. Driver attention is not p…
cs.CV2025
Interpretable Modeling of Driver Attention Shifts with a Vision-Language Model
Kaiser Hamid, Khandakar Ashrafi Akbar, Peihang Li +1
Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being moni…