Reliability-Calibrated Posterior Supervision for Weakly Supervised Semantic Segmentation

Abstract

Weakly supervised semantic segmentation (WSSS) with image-level labels is largely limited by the reliability of dense seed supervision. Existing CAM- and CLIP-based methods provide complementary localization cues, but their predictions are biased in different ways: classification-oriented cues are usually precise but incomplete, whereas alignment-oriented cues offer broader coverage but are more susceptible to contextual noise. In this letter, we propose Reliability-Calibrated Posterior Supervision (RCPS), a simple yet effective framework that uses discriminative classification evidence to calibrate broad CLIP-oriented cues. RCPS first constructs a semantic target through classification-regularized posterior calibration, avoiding direct commitment to either noisy alignment responses or incomplete classification activations. It then estimates pixel-wise reliability from cue self-certainty, classification–alignment consistency, and confidence-preserving disagreement, retaining potentially complementary evidence while suppressing uncertain responses. The resulting reliability-weighted objective learns dense seeds from calibrated soft targets. Experiments on PASCAL VOC 2012 and MS COCO 2014 show that RCPS consistently improves seed quality, pseudo-mask quality, and final segmentation performance over strong CAM- and CLIP-based baselines.