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Consensus aware foreground refinement network for collaborative camouflaged object detection

delete2025-09-07
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PRE
AI
H
Hongbo Bi
D
Disen Mo
S
Shiyuan Li
C
Cong Zhang *
DOI：10.1007/s00371-025-04155-6delete
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摘要

摘要

En 中文
在计算机视觉领域，在高度相似背景下准确检测伪装物体仍然是一个艰巨的挑战。现有的协同伪装物体检测（CoCOD）方法通常忽略了伪装物体组内的共表示信息。本文介绍了一种名为共识感知前景细化网络（CFNet）的新框架，旨在解决这一问题。CFNet包含一个共识感知模块（CAM）用于捕获图像间的共享语义线索，一个前景特征细化模块（FRM）用于增强前景表示，以及一个前景引导的完整性聚合解码器（FIAD）用于融合协同和细化后的前景特征。在CoCOD8K数据集上的实验结果表明，CFNet在协同伪装物体检测（CoCOD）、伪装物体检测（COD）和协同显著物体检测（CoSOD）方面均优于当前最优模型，实现了F-measure为0.750和E-measure为0.896的出色性能。这项工作突显了CFNet在复杂环境中提升协同伪装物体检测准确性的潜力。代码可从https://github.com/Firefly-m/CFNet获取。
Keyword:
Collaborative camouflaged object detection
Consensus awareness
Foreground refinement
Foreground-induced integrity aggregation decoder

期刊

Visual Computer 封面图
Visual Computer
IF:
2.9
论文数:
4.7K
被引数:
6.5K

机构

S
School of Electrical Information Engineering
学者数:
25
论文数: 7
被引数: 0
引用论文

引用论文

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