When Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation

Pan Liu; Jinshi Liu

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arXiv · 2509.16704

When Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation

Pan Liu·Jinshi Liu

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Abstract

While significant advances exist in pseudo-label generation for semi-supervised semantic segmentation, pseudo-label selection remains understudied. Existing methods typically use fixed confidence thresholds to retain high-confidence predictions as pseudo-labels. However, these methods cannot cope with network overconfidence tendency, where correct and incorrect predictions overlap significantly in high-confidence regions, making separation challenging and amplifying model cognitive bias. Meanwhile, the direct discarding of low-confidence predictions disrupts spatial-semantic continuity, causing critical context loss. We propose Confidence Separable Learning (CSL) to address these limitations. CSL formulates pseudo-label selection as a convex optimization problem within the confidence distribution feature space, establishing sample-specific decision boundaries to distinguish reliable from unreliable predictions. Additionally, CSL introduces random masking of reliable pixels to guide the network in learning contextual relationships from low-reliability regions, thereby mitigating the adverse effects of discarding uncertain predictions. Extensive experimental results on the Pascal, Cityscapes, and COCO benchmarks show that CSL performs favorably against state-of-the-art methods. Code and model weights are available at https://github.com/PanLiuCSU/CSL.

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Pan Liu, Jinshi Liu. 2025-09-20. When Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation. https://arxiv.org/abs/2509.16704

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