Yonghao Li

Rethinking IRSTD as a centroid regression problem with single-point supervision achieves competitive detection performance with significantly reduced computational cost, challenging the dominance of pixel-level segmentation approaches.

Overcome the limitations of existing information-theoretic methods by using structural entropy to learn high-order feature correlations, leading to improved feature selection in multi-view multi-label learning.