PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation

• URL: http://arxiv.org/abs/2604.15670v1
• Date: Fri, 17 Apr 2026 03:48:56 GMT
• Title: PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
• Abstract summary: Reasoning segmentation has expanded from ground-level scenes to remote-sensing imagery, yet UAV data poses distinct challenges.<n>We define the UAV Reasoning task and organize its semantic requirements into three dimensions: Spatial, Attribute, and Scene-level reasoning.<n>We construct DRSeg, a large-scale benchmark for UAV reasoning segmentation, containing 10k high-resolution aerial images paired with Chain-of-Thought QA supervision.
• Score: 71.53011159355401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning segmentation has recently expanded from ground-level scenes to remote-sensing imagery, yet UAV data poses distinct challenges, including oblique viewpoints, ultra-high resolutions, and extreme scale variations. To address these issues, we formally define the UAV Reasoning Segmentation task and organize its semantic requirements into three dimensions: Spatial, Attribute, and Scene-level reasoning. Based on this formulation, we construct DRSeg, a large-scale benchmark for UAV reasoning segmentation, containing 10k high-resolution aerial images paired with Chain-of-Thought QA supervision across all three reasoning types. As a benchmark companion, we introduce PixDLM, a simple yet effective pixel-level multimodal language model that serves as a unified baseline for this task. Experiments on DRSeg establish strong baseline results and highlight the unique challenges of UAV reasoning segmentation, providing a solid foundation for future research.

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