Tackling Ambiguity from Perspectives of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation

Zhiwei Yang, Yucong Meng, Kexue Fu, Shuo Wang☨, Zhijian song☨

IEEE Transactions on Multimedia (IF=8.4)

Abstract

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve dense predictions without laborious annotations. However, due to the ambiguous contexts and fuzzy regions, the performance of WSSS, particularly during the stages of generating Class Activation Maps (CAMs) and refining pseudo masks, is widely hindered by ambiguity. Despite this, this issue has received little attention in previous literature. In this work, we propose UniA, a unified single-staged WSSS framework, to efficiently tackle this issue from the perspectives of uncertainty inference and affinity diversification. When activating class objects, we argue that the false activation stems from the bias to ambiguous regions during the feature extraction. Therefore, we formulate a robust feature representation with a Gaussian distribution and introduce the uncertainty estimation to avoid the bias. A distribution loss is proposed to supervise the process, which effectively captures the ambiguity and models the complex dependencies among features. When refining pseudo labels, we observe that the affinity from the prevailing refinement methods intends to be overly similar among ambiguities. To this end, we design an affinity diversification module to promote diversity among semantics. A mutual complementing refinement is first proposed to statically rectify the ambiguous affinity with multiple inferred pseudo labels. Then a contrastive affinity loss is further designed to dynamically diversify the relations among unrelated semantics. It stably propagates the diversity into the feature representation and helps generate better pseudo masks. Extensive experiments are conducted on PASCAL VOC, MS COCO, and medical ACDC datasets, which validate the efficiency of UniA tackling ambiguity and its superiority over recent single-staged or even most multi-staged competitors.

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