Unsupervised Camouflaged Object Detection

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8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection
Sep 30, 2026Junyang Xia, Luocheng Zhang, Wenwen Pan +4Multi-Scale Feature Fusion Unsupervised Camouflaged Object Detection
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum
Aug 31, 2026Avi Gupta, Trasha GuptaMultispectral Object Detection Unsupervised Camouflaged Object Detection
Indraprastha Institute of Information Technology, Delhi · Delhi Technological University, Delhi
Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection
Aug 11, 2026Huafeng Chen, Yueming Lyu, Chenyang Si +3Unsupervised Camouflaged Object Detection Object Detection
School of Intelligence Science and Technology, Nanjing University · Department of Computing, Imperial College London
LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection
Aug 8, 2026Shangye Song, Tianzhi Zhu, Syed Ariff Syed Hesham +2Text-Guided Image Segmentation Unsupervised Camouflaged Object Detection
School of Computer Science, The University of Auckland · VCIP, College of Computer Science, Nankai University · School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore +4
COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions
May 23, 2026Arafat Hossain SayemImage Segmentation Image Corruption Robustness
EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
Apr 13, 2026Ye Wang, Kai Huang, Sumin Shen +1Unsupervised Camouflaged Object Detection Camouflaged Object Detection
West China Hospital, Sichuan University · Independent Researcher · Department of Data Science and Artificial Intelligence, Auckland University of Technology
Language-Guided Structure-Aware Network for Camouflaged Object Detection
Mar 25, 2026Min ZhangUnsupervised Camouflaged Object Detection Camouflaged Object Detection
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401120, China
EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection
Mar 12, 2026Shuo Jiang, Gaojia Zhang, Min Tan +2Pseudo-Labeling Unsupervised Camouflaged Object Detection
Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University · Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University · College of Computer Science and Technology, Zhejiang University

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Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection

Junyang Xia, Luocheng Zhang, Wenwen Pan, Chifeng Zhu+3

School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China

Multi-Scale Feature Fusion Unsupervised Camouflaged Object Detection Camouflaged Object Detection

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Reference-guided camouflaged object detection aims to segment a target whose visual appearance closely resembles its surroundings by exploiting auxiliary reference samples. The task remains difficult because reference samples contain inconsistent target cues, while generic visual representations are not inherently aligned with the target specified by the references. To handle these problems, we present a consensus-aware multi-source fusion framework. Reference-Conditioned Dual-Backbone Fusion (RCDF) couples trainable PVTv2 query features with frozen DINOv3 representations and uses reference-conditioned correlation to select foundation-model evidence before multi-scale fusion. The framework also aggregates multiple references through cross-reference consensus aggregation and injects reference information at semantic depths matched to the query features. Extensive experiments demonstrate the effectiveness of the proposed method. The results further show that reference consensus, target-conditioned foundation features, and hierarchical decoding provide complementary improvements under the evaluation protocol. The source code will be made publicly available upon acceptance.