Abstract
Semi-supervised remote sensing (RS) image semantic segmentation offers a promising solution to alleviate the burden of exhaustive annotation, yet it fundamentally struggles with pseudo-label drift, a phenomenon where confirmation bias leads to the accumulation of errors during training. In this work, we propose Co2S, a stable semi-supervised RS segmentation framework that synergistically fuses priors from vision-language models and self-supervised models. Specifically, we construct a heterogeneous dual-student architecture comprising two distinct ViT-based vision foundation models initialized with pretrained CLIP and DINOv3 to mitigate error accumulation and pseudo-label drift. To effectively incorporate these distinct priors, an explicit-implicit semantic co-guidance mechanism is introduced that utilizes text embeddings and learnable queries to provide explicit and implicit class-level guidance, respectively, thereby jointly enhancing semantic consistency. Furthermore, a global-local feature collaborative fusion strategy is developed to effectively fuse the global contextual information captured by CLIP with the local details produced by DINOv3, enabling the model to generate highly precise segmentation results. Extensive experiments on six popular datasets demonstrate the superiority of the proposed method, which consistently achieves leading performance across various partition protocols and diverse scenarios. Project page is available at this https URL.
Abstract (translated)
半监督遥感（RS）图像语义分割提供了一种有前景的解决方案，以减轻详尽标注的工作负担。然而，它在训练过程中面临着伪标签漂移的问题，这是一种由确认偏差导致错误积累的现象。在这项工作中，我们提出了Co2S，这是一个稳定的半监督RS分割框架，该框架将视觉语言模型和自监督模型中的先验知识进行了协同融合。具体而言，我们构建了一个异构双学生架构，其中包括两个基于ViT的视觉基础模型，并分别用预训练的CLIP和DINOv3进行初始化，以减轻错误积累和伪标签漂移的问题。 为了有效整合这些不同的先验知识，引入了显式隐式语义协同引导机制。该机制利用文本嵌入为明确类别提供指导，同时通过可学习查询提供隐式的类别级指导，从而共同提升语义一致性。 此外，开发了一种全局局部特征协作融合策略，以有效地将CLIP捕获的全球上下文信息与DINOv3产生的本地细节进行融合。这使模型能够生成高度精确的分割结果。 在六个流行数据集上的广泛实验表明了所提出方法的优势，在各种分区协议和多样化场景中均取得了领先性能。项目页面可在提供的链接查看。
URL
https://arxiv.org/abs/2512.23035
