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InDReCT: Intra-domain Dual Reconstruction for Cross-domain Transfer in Camouflaged Object Detection

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成果类型:
期刊论文
作者:
Guowen Yue;Ge Jiao*;Fangyan Wang
通讯作者:
Ge Jiao
作者机构:
[Guowen Yue; Fangyan Wang] 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:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2025
页码:
114334
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:
Current camouflaged object detection (COD) methods primarily rely on a direct mapping from image to mask. However, due to the inherent semantic and structural gap between the image and its corresponding mask, the learned feature representations often exhibit poor generalization ability. To address this issue, we propose a novel intra-domain dual reconstruction framework, termed InDReCT, which reformulates the image-to-mask prediction as a cross-domain transfer task by simultaneously reconstructing both the input image and its corresponding mask...

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