Abstract
Training-free open-vocabulary remote sensing segmentation (OVRSS), empowered by vision-language models, has emerged as a promising paradigm for achieving category-agnostic semantic understanding in remote sensing imagery. Existing approaches mainly focus on enhancing feature representations or mitigating modality discrepancies to improve patch-level prediction accuracy. However, such independent prediction schemes are fundamentally misaligned with the intrinsic characteristics of remote sensing data. In real-world applications, remote sensing scenes are typically large-scale and exhibit strong spatial as well as semantic correlations, making isolated patch-wise predictions insufficient for accurate segmentation. To address this limitation, we propose ConInfer, a context-aware inference framework for OVRSS that performs joint prediction across multiple spatial units while explicitly modeling their inter-unit semantic dependencies. By incorporating global contextual cues, our method significantly enhances segmentation consistency, robustness, and generalization in complex remote sensing environments. Extensive experiments on multiple benchmark datasets demonstrate that our approach consistently surpasses state-of-the-art per-pixel VLM-based baselines such as SegEarth-OV, achieving average improvements of 2.80% and 6.13% on open-vocabulary semantic segmentation and object extraction tasks, respectively. The implementation code is available at: this https URL
Abstract (translated)
无需训练的开放词汇遥感分割（OVRSS）得益于视觉语言模型，已成为实现遥感影像类别无关语义理解的有前景范式。现有方法主要聚焦于增强特征表示或缓解模态差异以提升图像块级预测精度。然而此类独立预测方案与遥感数据的内在特性存在根本错配——真实场景中遥感影像通常规模庞大且呈现强烈的空间与语义关联性，孤立的图像块预测难以实现精确分割。为突破此局限，我们提出ConInfer，一种面向OVRSS的上下文感知推理框架，通过跨多空间单元联合预测并显式建模单元间语义依赖。借助全局上下文信息的融入，本方法在复杂遥感环境中显著提升了分割一致性、鲁棒性与泛化能力。在多个基准数据集上的大量实验表明，本方法持续超越SegEarth-OV等先进逐像素视觉语言模型基线，在开放词汇语义分割与目标提取任务中分别实现平均2.80%与6.13%的性能提升。实现代码已公开于：此超链接
URL
https://arxiv.org/abs/2603.29271
