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English(EN) Decoupled Motion Representation Learning for Moving Infrared Small Target Detection

新框架增强了动态场景下的红外小目标检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的框架，用于在动态场景中检测红外小目标，解决了目标、成像平台和背景之间运动耦合的挑战。该方法引入了一种解耦的运动表示学习方法，将全局相干运动动力学与目标敏感的局部运动异常分离开来。该框架利用预训练的光流先验知识和一种保持结构的自监督适应策略来实现红外运动对应。在基准数据集上的实验表明，该方法在复杂动态场景下优于现有的最先进方法，同时保持了高效的推理速度。 AI

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了一种新的技术方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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新框架增强了动态场景下的红外小目标检测能力

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了一种新的技术方法。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Guoyi Zhang, Peiwen Wu, Han Wang, Xiangpeng Xu, Xiaohu Zhang ·

    用于运动红外小目标检测的解耦运动表示学习

    arXiv:2606.15286v1 Announce Type: new Abstract: Infrared small target detection in dynamic scenes remains challenging due to the highly coupled motions among targets, imaging platforms, and dynamic backgrounds. Existing multi-frame methods usually perform implicit temporal modeli…

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