More observation leads to more clarity: Multi-view collaboration network for camouflaged object detection

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Fangyan Wang;Ge Jiao*;Guowen Yue

通讯作者：

Ge Jiao

作者机构：

[Fangyan Wang; Guowen Yue] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, Hunan, China

Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, Hunan, China

Hunan Engineering Research Center of Cyberspace Security Technology and Applications, Hengyang Normal University, Hengyang, 421002, Hunan, China

[Ge Jiao] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, Hunan, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, Hunan, China<&wdkj&>Hunan Engineering Research Center of Cyberspace Security Technology and Applications, Hengyang Normal University, Hengyang, 421002, Hunan, China

通讯机构：

[Ge Jiao] C

College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, Hunan, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, Hunan, China<&wdkj&>Hunan Engineering Research Center of Cyberspace Security Technology and Applications, Hengyang Normal University, Hengyang, 421002, Hunan, China

语种：

英文

期刊：

Neurocomputing

ISSN：

0925-2312

年：

2025

页码：

130433

DOI：

10.1016/j.neucom.2025.130433

基金类别：

CRediT authorship contribution statement Fangyan Wang: Writing – original draft, Methodology, Data curation, Conceptualization. Ge Jiao: Writing – review & editing, Supervision, Project administration. Guowen Yue: Project administration, Investigation, acquisition.

机构署名：

本校为第一且通讯机构

院系归属：

计算机科学与技术学院

物理与电子工程学院

摘要：

Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the Multi-view Collaboration Network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head at...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

More observation leads to more clarity: Multi-view collaboration network for camouflaged object detection

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问