Open-Vocabulary Semantic Segmentation in Remote Sensing via Hierarchical Attention Masking and Model Composition

• URL: http://arxiv.org/abs/2602.23869v1
• Date: Fri, 27 Feb 2026 10:11:12 GMT
• Title: Open-Vocabulary Semantic Segmentation in Remote Sensing via Hierarchical Attention Masking and Model Composition
• Authors: Mohammadreza Heidarianbaei, Mareike Dorozynski, Hubert Kanyamahanga, Max Mehltretter, Franz Rottensteiner,
• Abstract summary: ReSeg-CLIP is a new training-free Open-Vocabulary Semantic method for remote sensing data.<n>Our method achieves state-of-the-art results across three RS benchmarks without additional training.
• Score: 1.0019706819513459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose ReSeg-CLIP, a new training-free Open-Vocabulary Semantic Segmentation method for remote sensing data. To compensate for the problems of vision language models, such as CLIP in semantic segmentation caused by inappropriate interactions within the self-attention layers, we introduce a hierarchical scheme utilizing masks generated by SAM to constrain the interactions at multiple scales. We also present a model composition approach that averages the parameters of multiple RS-specific CLIP variants, taking advantage of a new weighting scheme that evaluates representational quality using varying text prompts. Our method achieves state-of-the-art results across three RS benchmarks without additional training.

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