Abstract
Most existing methods for training-free Open-Vocabulary Semantic Segmentation (OVSS) are based on CLIP. While these approaches have made progress, they often face challenges in precise localization or require complex pipelines to combine separate modules, especially in remote sensing scenarios where numerous dense and small targets are present. Recently, Segment Anything Model 3 (SAM 3) was proposed, unifying segmentation and recognition in a promptable framework. In this paper, we present a preliminary exploration of applying SAM 3 to the remote sensing OVSS task without any training. First, we implement a mask fusion strategy that combines the outputs from SAM 3's semantic segmentation head and the Transformer decoder (instance head). This allows us to leverage the strengths of both heads for better land coverage. Second, we utilize the presence score from the presence head to filter out categories that do not exist in the scene, reducing false positives caused by the vast vocabulary sizes and patch-level processing in geospatial scenes. We evaluate our method on extensive remote sensing datasets. Experiments show that this simple adaptation achieves promising performance, demonstrating the potential of SAM 3 for remote sensing OVSS. Our code is released at this https URL.
Abstract (translated)
大多数现有的无需训练的开放词汇语义分割（OVSS）方法都基于CLIP。尽管这些方法已经取得了一定的进步，但它们在精确定位上往往面临挑战，并且通常需要复杂的管道来结合单独的模块，尤其是在存在众多密集和小型目标的遥感场景中更是如此。最近提出了Segment Anything Model 3 (SAM 3)，它在一个可提示的框架内统一了分割和识别任务。在这篇论文中，我们展示了将SAM 3应用于无需训练的遥感OVSS任务的一个初步探索。 首先，我们实现了一种掩码融合策略，该策略结合了来自SAM 3语义分割头和Transformer解码器（实例头）的输出。这使我们可以利用两个头部的优点来更好地覆盖土地范围。 其次，我们使用存在评分从存在性头部过滤掉场景中不存在的类别，从而减少由于庞大词汇量以及地理空间场景中的补丁级处理所导致的假阳性情况。 我们在广泛的遥感数据集上评估了我们的方法。实验结果表明，这种简单的适应达到了令人鼓舞的效果，展示了SAM 3在遥感OVSS任务上的潜力。我们的代码可在[此处](https://this-url.com)获取。
URL
https://arxiv.org/abs/2512.08730
