Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
This work addresses the challenge of reducing annotation dependence in semantic segmentation for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of error-prone pseudo-labeling in semi-supervised semantic segmentation by introducing Fuzzy Positive Learning (FPL), which adaptively encourages fuzzy positive predictions and suppresses negatives, achieving consistent performance gains on Cityscapes and VOC 2012 datasets.
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach.