English(EN) Residual Optimal Transport-Based Experts Collaboration Towards Modality-Aware Infrared-Visible Object Detection

新的FlexibleFusion方法使目标检测能够适应缺失的传感器数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了FlexibleFusion，一种新颖的红外-可见光目标检测（IVOD）方法，能够适应缺失的传感器数据。该方法利用模态感知专家协作（MAEC）机制，根据传感器可用性动态地在跨模态融合和自融合之间切换。此外，还引入了残差自步长熵最优传输（RSPEOT）技术，通过优先处理可靠匹配并逐步优化更具挑战性的匹配来对齐不同模态的特征分布。 AI

影响该方法通过处理间歇性传感器数据提高了目标检测系统的鲁棒性，有可能增强实际应用中的性能。

排序理由该集群包含一篇详细介绍新技术的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yue Zhao, Hua Yu, Yukun Zhao, Yuzhi Zhang, Maoguo Gong, Xin Mei, Zhuping Hu, Yanchi Li, A. K. Qin · 2026-09-04 04:00

基于残差最优传输的专家协作实现模态感知红外-可见光目标检测

arXiv:2609.03516v1 Announce Type: new Abstract: Infrared-visible object detection (IVOD) integrates complementary evidence from visible and infrared sensors for reliable perception in challenging scenes. In practice, sensors may fail or drop frames, leaving one modality unavailab…

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报道来源 [1]

基于残差最优传输的专家协作实现模态感知红外-可见光目标检测

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