提出自适应空间增强方法，提升半监督语义分割性能。
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation
- 根据图像熵动态调整空间增强强度，实现自适应
- 在PASCAL VOC、Cityscapes等数据集上显著提升精度
- 可无缝集成现有方法，适合改进半监督分割模型
在半监督语义分割（SSSS）中，数据增强对弱-强一致性正则化框架至关重要，能提升多样性并增强模型泛化能力。近期的强增强方法多聚焦于强度扰动，对语义掩码影响较小；而空间增强如平移、旋转虽在有监督任务中表现优异，却常被忽略。本文证明，尽管空间增强会导致弱/强增强间掩码不一致，仍可有效促进模型训练。针对图像间差异性，提出基于熵的自适应增强策略，动态调整每张图像的增强强度。大量实验表明，所提自适应空间增强（ASAug）可作为即插即用模块，持续提升现有方法性能，在PASCAL VOC 2012、Cityscapes、COCO等基准上达到当前最优结果。
原文摘要 · Abstract (English)
In semi-supervised semantic segmentation (SSSS), data augmentation plays a crucial role in the weak-to-strong consistency regularization framework, as it enhances diversity and improves model generalization. Recent strong augmentation methods have primarily focused on intensity-based perturbations, which have minimal impact on the semantic masks. In contrast, spatial augmentations like translation and rotation have long been acknowledged for their effectiveness in supervised semantic segmentation tasks, but they are often ignored in SSSS. In this work, we demonstrate that spatial augmentation can also contribute to model training in SSSS, despite generating inconsistent masks between the weak and strong augmentations. Furthermore, recognizing the variability among images, we propose an adaptive augmentation strategy that dynamically adjusts the augmentation for each instance based on entropy. Extensive experiments show that our proposed Adaptive Spatial Augmentation (\textbf{ASAug}) can be integrated as a pluggable module, consistently improving the performance of existing methods and achieving state-of-the-art results on benchmark datasets such as PASCAL VOC 2012, Cityscapes, and COCO.
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