Diversity augmentation and multi-fuzzy label for semi-supervised semantic segmentation
Zhenyan Wang, Zhenxue Chen, Chengyun Liu, Yaping Zhao, Xinming Zhu, Q. M. Jonathan Wu · Neurocomputing · 2025
Semantic segmentation aims to provide pixel-wise accurate predictions for images. Semi-supervised semantic segmentation aims to learn a semantic segmentation model using a limited number of labeled images and a large fraction of unlabeled images. Existing methods primarily focus on introducing additional models or complex training procedures but overlook the model itself and such complex strategies tend to discard many usable pixels, exacerbating the class imbalance problem . In this paper, we propose DAM for semi-supervised semantic segmentation, a simple yet effective method that mainly focuses on the inputs and outputs of the model itself. For the input component, we posit that diverse data augmentations can provide more semantic information . Therefore, we propose a method called Random Diversity Augmentations. Given an unlabeled image, we apply different triple-level data augmentations to provide more semantic information. For the output component, our approach is inspired by the fact that many unreliable predictions are confused only among the top classes rather than all classes, so we contend that fuzzy pixels can still provide valuable guidance to the model. Specifically, we select fuzzy pixels based on confidence and assign multi-fuzzy labels to these pixels for training the model, which allows us to leverage the information more effectively. Our straightforward DAM achieves new state-of-the-art performance on SSS different benchmarks. Code is available at https://github.com/Wang-zhenyan/DAM .