用双教师机制提升小样本下的语义分割精度
IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme
- 分全球与局部双路径学习，融合高层语义与细节特征
- 在仅少量标注数据下，分割准确率超越现有方法
- 适合标注稀缺场景的图像分割任务
半监督语义分割旨在利用少量标注图像和大量未标注数据提升分割精度。现有方法多依赖伪标签、一致性正则化和协同训练，但难以兼顾全局语义与细粒度局部特征。为此，我们提出一种三分支半监督分割框架IGL-DT，采用双教师策略。该方法使用SwinUnet进行全局上下文学习以获取高层语义，同时通过ResUnet实现局部区域学习以细化细节特征。此外，引入差异学习机制，降低对单一教师的依赖，促进自适应特征学习。在多个基准数据集上的大量实验表明，本方法在不同数据规模下均优于当前最优方法，显著提升分割性能。
原文摘要 · Abstract (English)
Semi-Supervised Semantic Segmentation (SSSS) aims to improve segmentation accuracy by leveraging a small set of labeled images alongside a larger pool of unlabeled data. Recent advances primarily focus on pseudo-labeling, consistency regularization, and co-training strategies. However, existing methods struggle to balance global semantic representation with fine-grained local feature extraction. To address this challenge, we propose a novel tri-branch semi-supervised segmentation framework incorporating a dual-teacher strategy, named IGL-DT. Our approach employs SwinUnet for high-level semantic guidance through Global Context Learning and ResUnet for detailed feature refinement via Local Regional Learning. Additionally, a Discrepancy Learning mechanism mitigates over-reliance on a single teacher, promoting adaptive feature learning. Extensive experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches, achieving superior segmentation performance across various data regimes.
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