[Publication] FSDAM Accepted at WACV 2027

We are pleased to share that FSDAM (Few-Shot Driver Attention Modeling) has been accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2027).

The work is a collaboration between Texas Tech University, Purdue University, and Towson University, led by Kaiser Hamid with Can Cui, Khandakar Ashrafi Akbar, Ziran Wang, and Nade Liang.

FSDAM studies not only where drivers look, but also why their attention shifts. The framework jointly predicts spatial driver attention and generates structured reasoning explanations using only a small number of annotated examples. It decomposes attention into interpretable components including scene context, current focus, anticipated next focus, and causal reasoning.

The project combines a spatial prediction branch with a language-based reasoning branch, while using training-time vision–language alignment to inject semantic priors without increasing inference complexity.

Project page: FSDAM