Small Objects Matters in Weakly-supervised Semantic Segmentation
Cheolhyun Mun Sanghuk Lee Youngjung Uh Junsuk Choe Hyeran Byun
Weakly-supervised semantic segmentation (WSSS) performs pixel-wise
classification given only image-level labels for training. Despite the
difficulty of this task, the research community has achieved promising results
over the last five years. Still, current WSSS literature misses the detailed
sense of how well the methods perform on different sizes of objects. Thus we
propose a novel evaluation metric to provide a comprehensive assessment across
different object sizes and collect a size-balanced evaluation set to complement
PASCAL VOC. With these two gadgets, we reveal that the existing WSSS methods
struggle in capturing small objects. Furthermore, we propose a size-balanced
cross-entropy loss coupled with a proper training strategy. It generally
improves existing WSSS methods as validated upon ten baselines on three
different datasets.