English(EN) LGFN: Lightweight Gated RGB-Polarization Fusion with Modality-Availability Conditioning for Camouflaged Object Detection

新的LGFN框架融合RGB和偏振数据用于伪装目标检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了LGFN，一种用于伪装目标检测的新型框架，可有效融合RGB和偏振成像数据。该轻量级系统旨在适应不同的输入条件，允许优化仅RGB或偏振辅助配置。该框架包括一个模态路由器来选择合适的设置，以及一个模态门来校准偏振输入。在评估中，仅RGB配置在PCOD_1200数据集上取得了最先进的成果，而多模态配置在准确性和效率方面显著优于现有方法，减少了参数数量和延迟。 AI

影响这项研究通过实现不同成像模态更有效、更准确的融合，推动了伪装目标检测的发展，有望改进监控和自主系统中的应用。

排序理由该集群描述了一篇关于计算机视觉任务的新型技术框架的最新研究论文。[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) · Zhuangfan Huang, Xiaosong Li, Yang Liu, Tao Ye, Haishu Tan · 2026-09-14 04:00

LGFN：轻量级门控RGB-偏振融合与模态可用性条件化用于伪装目标检测

arXiv:2609.12798v1 Announce Type: new Abstract: Camouflaged object detection (COD) is an important engineering task in intelligent optical perception, but it remains challenging when targets closely resemble their surroundings. Polarization imaging provides complementary physical…

本文如何被排名

报道来源 [1]

LGFN：轻量级门控RGB-偏振融合与模态可用性条件化用于伪装目标检测

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