针对碲锌镉半导体图像低对比度缺陷边界，提出分组一致性增强框架提升半监督分割效果。
Harnessing Group-Oriented Consistency Constraints for Semi-Supervised Semantic Segmentation in CdZnTe Semiconductors
- 基于多视角共享真值的分组特性，设计组内一致性增强机制
- 仅用2组标注数据（0.5%）即达70.6% mIoU，显著优于传统方法
- 适合低对比度缺陷分割任务，尤其适用于材料显微图像分析
由于碲锌镉（CdZnTe）半导体图像缺陷边界对比度低，标注需依赖多视角交叉验证，各视角共享同一真值，形成独特的“多对一”关系。现有半监督语义分割方法多基于“一对一”假设，难以适应此特性，易在低对比度区域累积误差并加剧确认偏差。为此，本文从分组视角重构半监督流程，提出人类启发式方案：组内一致性增强框架（ICAF）。首先通过实验验证组内固有的一致性约束，建立基于组内视图采样的基线（IVS）。在此基础上，引入伪标签修正网络（PCN），包含视图增强模块（VAM）与视图修正模块（VCM）。VAM通过聚合多视角动态合成边界感知视图以增强边缘细节；VCM则将合成视图与其他视图交互，强化显著区域、抑制噪声。大量实验表明该方法在CdZnTe数据集上表现优异。采用DeepLabV3+ + ResNet-101作为分割模型，仅使用2组标注数据（0.5%）即达到70.6% mIoU。代码已开源。
原文摘要 · Abstract (English)
Labeling Cadmium Zinc Telluride (CdZnTe) semiconductor images is challenging due to the low-contrast defect boundaries, necessitating annotators to cross-reference multiple views. These views share a single ground truth (GT), forming a unique ``many-to-one'' relationship. This characteristic renders advanced semi-supervised semantic segmentation (SSS) methods suboptimal, as they are generally limited by a ``one-to-one'' relationship, where each image is independently associated with its GT. Such limitation may lead to error accumulation in low-contrast regions, further exacerbating confirmation bias. To address this issue, we revisit the SSS pipeline from a group-oriented perspective and propose a human-inspired solution: the Intra-group Consistency Augmentation Framework (ICAF). First, we experimentally validate the inherent consistency constraints within CdZnTe groups, establishing a group-oriented baseline using the Intra-group View Sampling (IVS). Building on this insight, we introduce the Pseudo-label Correction Network (PCN) to enhance consistency representation, which consists of two key modules. The View Augmentation Module (VAM) improves boundary details by dynamically synthesizing a boundary-aware view through the aggregation of multiple views. In the View Correction Module (VCM), this synthesized view is paired with other views for information interaction, effectively emphasizing salient regions while minimizing noise. Extensive experiments demonstrate the effectiveness of our solution for CdZnTe materials. Leveraging DeepLabV3+ with a ResNet-101 backbone as our segmentation model, we achieve a 70.6\% mIoU on the CdZnTe dataset using only 2 group-annotated data (5\textperthousand). The code is available at \href{https://github.com/pipixiapipi/ICAF}{https://github.com/pipixiapipi/ICAF}.
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