3 papers
cs.CV2025
Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation
Zhongwen Zhang, Yuri Boykov
We consider weakly supervised segmentation where only a fraction of pixels have ground truth labels (scribbles) and focus on a self-labeling approach optimizing relaxations of the…
cs.CV2025
Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation
Xingye Fan, Zhongwen, Zhang +1
This paper demonstrates a surprising result for segmentation with image-level targets: extending binary class tags to approximate relative object-size distributions allows off-the-…
cs.LG2023
Collision Cross-entropy for Soft Class Labels and Deep Clustering
Zhongwen Zhang, Yuri Boykov
We propose "collision cross-entropy" as a robust alternative to Shannon's cross-entropy (CE) loss when class labels are represented by soft categorical distributions y. In general,…