{"ID":2895190,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09556","arxiv_id":"2507.09556","title":"SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing","abstract":"Due to the limitation of the optical lens focal length and the resolution of the infrared detector, distant Closely-Spaced Infrared Small Target (CSIST) groups typically appear as mixing spots in the infrared image. In this paper, we propose a novel task, Sequential CSIST Unmixing, namely detecting all targets in the form of sub-pixel localization from a highly dense CSIST group. However, achieving such precise detection is an extremely difficult challenge. In addition, the lack of high-quality public datasets has also restricted the research progress. To this end, firstly, we contribute an open-source ecosystem, including SeqCSIST, a sequential benchmark dataset, and a toolkit that provides objective evaluation metrics for this special task, along with the implementation of 23 relevant methods. Furthermore, we propose the Deformable Refinement Network (DeRefNet), a model-driven deep learning framework that introduces a Temporal Deformable Feature Alignment (TDFA) module enabling adaptive inter-frame information aggregation. To the best of our knowledge, this work is the first endeavor to address the CSIST Unmixing task within a multi-frame paradigm. Experiments on the SeqCSIST dataset demonstrate that our method outperforms the state-of-the-art approaches with mean Average Precision (mAP) metric improved by 5.3\\%. Our dataset and toolkit are available from https://github.com/GrokCV/SeqCSIST.","short_abstract":"Due to the limitation of the optical lens focal length and the resolution of the infrared detector, distant Closely-Spaced Infrared Small Target (CSIST) groups typically appear as mixing spots in the infrared image. In this paper, we propose a novel task, Sequential CSIST Unmixing, namely detecting all targets in the f...","url_abs":"https://arxiv.org/abs/2507.09556","url_pdf":"https://arxiv.org/pdf/2507.09556v1","authors":"[\"Ximeng Zhai\",\"Bohan Xu\",\"Yaohong Chen\",\"Hao Wang\",\"Kehua Guo\",\"Yimian Dai\"]","published":"2025-07-13T09:59:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":612164,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2895190,"paper_url":"https://arxiv.org/abs/2507.09556","paper_title":"SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing","repo_url":"https://github.com/GrokCV/SeqCSIST","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}