DGL-RSIS：解耦全局空间上下文与局部类别语义的无训练遥感图像分割
DGL-RSIS: Decoupling Global Spatial Context and Local Class Semantics for Training-Free Remote Sensing Image Segmentation
AI总结：
针对VLMs迁移至遥感分割的领域差距与任务多样性挑战，提出无训练统一框架DGL-RSIS，通过解耦视觉文本表示并在局部语义和全局上下文层面对齐，在OVSS和RES任务上优于现有无训练方法。
摘要：
视觉语言模型（VLMs）的出现弥合了视觉与语言之间的鸿沟，实现了超越传统纯视觉深度学习模型的多模态理解。然而，由于领域差距大且遥感（RS）输入在任务间具有多样性，特别是在开放词汇语义分割（OVSS）和指代表达分割（RES）中，将VLMs从自然图像领域迁移到遥感分割仍具挑战。本文提出一种无训练统一框架DGL-RSIS，它解耦视觉与文本表示，并在局部语义和全局上下文层面进行视觉-语言对齐。具体而言，全局-局部解耦（GLD）模块将文本输入分解为局部语义标记和全局上下文标记，同时将图像输入划分为类别无关的掩码提案。然后，局部视觉-文本对齐（LVTA）模块从掩码提案中自适应提取上下文感知视觉特征，并通过知识引导的提示工程丰富文本特征，从局部视角实现OVSS。此外，全局视觉-文本对齐（GVTA）模块采用全局增强的Grad-CAM机制捕获指代表达的上下文线索，随后通过掩码选择模块将像素级激活整合为掩码级分割输出，从全局视角实现RES。在iSAID（OVSS）和RRSIS-D（RES）基准上的实验表明，DGL-RSIS优于现有无训练方法。消融研究进一步验证了各模块的有效性。据我们所知，这是首个用于遥感图像分割的无训练统一框架，可在无需额外训练的情况下将自然图像上训练的VLMs的语义能力有效迁移到遥感领域。
英文摘要：
The emergence of vision language models (VLMs) bridges the gap between vision and language, enabling multimodal understanding beyond traditional visual-only deep learning models. However, transferring VLMs from the natural image domain to remote sensing (RS) segmentation remains challenging due to the large domain gap and the diversity of RS inputs across tasks, particularly in open-vocabulary semantic segmentation (OVSS) and referring expression segmentation (RES). Here, we propose a training-free unified framework, termed DGL-RSIS, which decouples visual and textual representations and performs visual-language alignment at both local semantic and global contextual levels. Specifically, a Global-Local Decoupling (GLD) module decomposes textual inputs into local semantic tokens and global contextual tokens, while image inputs are partitioned into class-agnostic mask proposals. Then, a Local Visual-Textual Alignment (LVTA) module adaptively extracts context-aware visual features from the mask proposals and enriches textual features through knowledge-guided prompt engineering, achieving OVSS from a local perspective. Furthermore, a Global Visual-Textual Alignment (GVTA) module employs a global-enhanced Grad-CAM mechanism to capture contextual cues for referring expressions, followed by a mask selection module that integrates pixel-level activations into mask-level segmentation outputs, thereby achieving RES from a global perspective. Experiments on the iSAID (OVSS) and RRSIS-D (RES) benchmarks demonstrate that DGL-RSIS outperforms existing training-free approaches. Ablation studies further validate the effectiveness of each module. To the best of our knowledge, this is the first unified training-free framework for RS image segmentation, which effectively transfers the semantic capability of VLMs trained on natural images to the RS domain without additional training.
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