Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Understand

Weakly-Supervised Semantic Segmentation (WSSS) segments objects without a heavy burden of dense annotation.

• While as a price, generated pseudo-masks exist obvious noisy pixels, which result in sub-optimal segmentation models trained over these pseudo-masks.
• But rare studies notice or work on this problem, even these noisy pixels are inevitable after their improvements on pseudo-mask.
• So we try to improve WSSS in the aspect of noise mitigation.