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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.15670 |
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| _version_ | 1866914482303795200 |
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| author | Ke, Shuyan Mei, Yifan Wu, Changli Zheng, Yonghan Ji, Jiayi Cao, Liujuan Ji, Rongrong |
| author_facet | Ke, Shuyan Mei, Yifan Wu, Changli Zheng, Yonghan Ji, Jiayi Cao, Liujuan Ji, Rongrong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15670 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation Ke, Shuyan Mei, Yifan Wu, Changli Zheng, Yonghan Ji, Jiayi Cao, Liujuan Ji, Rongrong Computer Vision and Pattern Recognition 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. |
| title | PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.15670 |