In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version. Intriguingly, we observe that such a simple pipeline already achieves competitive results against recent advanced works, when transferred to our segmentation scenario. Its success heavily relies on the manual design of strong data augmentations, however, which may be limited and inadequate to explore a broader perturbation space. Motivated by this, we propose an auxiliary feature perturbation stream as a supplement, leading to an expanded perturbation space. On the other, to sufficiently probe original image-level augmentations, we present a dual-stream perturbation technique, enabling two strong views to be simultaneously guided by a common weak view. Consequently, our overall Unified Dual-Stream Perturbations approach (UniMatch) surpasses all existing methods significantly across all evaluation protocols on the Pascal, Cityscapes, and COCO benchmarks. We also demonstrate the superiority of our method in remote sensing interpretation and medical image analysis. Code is available at https://github.com/LiheYoung/UniMatch.

本文介绍了一种基于弱到强一致性框架的半监督分类技术，将图像的弱扰动版本作为强扰动版本的监督信息，通过使用数据扩充，提出了一个统一的双流扰动方法，该方法在各种基准测试中表现优异。

半监督语义分割中弱到强一致性的再访