Camouflaged object detection has attracted a lot of attention in computer vision. The main challenge lies in the high degree of similarity between camouflaged objects and their surroundings in the spatial domain, making identification difficult. Existing methods attempt to reduce the impact of pixel similarity by maximizing the distinguishing ability of spatial features with complicated design, but often ignore the sensitivity and locality of features in the spatial domain, leading to sub-optimal results. In this paper, we propose a new approach to address this issue by jointly exploring the representation in the frequency and spatial domains, introducing the Frequency-Spatial Entanglement Learning (FSEL) method. This method consists of a series of well-designed Entanglement Transformer Blocks (ETB) for representation learning, a Joint Domain Perception Module for semantic enhancement, and a Dual-domain Reverse Parser for feature integration in the frequency and spatial domains. Specifically, the ETB utilizes frequency self-attention to effectively characterize the relationship between different frequency bands, while the entanglement feed-forward network facilitates information interaction between features of different domains through entanglement learning. Our extensive experiments demonstrate the superiority of our FSEL over 21 state-of-the-art methods, through comprehensive quantitative and qualitative comparisons in three widely-used datasets. The source code is available at: https://github.com/CSYSI/FSEL.
Frequency-Guided Spatial Adaptation for Camouflaged Object Detection
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地一体化大数据应用技术国家工程实验室）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；School of Information and Technology, Northwest University（西北大学信息与技术学院）；Xi’an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University（西京学院智能康复人机融合控制技术西安市重点实验室）
A Revisit to the Decoder for Camouflaged Object Detection
Graduate School of Data Science, Seoul National University（首尔国立大学数据科学研究生院）；Department of Intelligence and Information, Seoul National University（首尔国立大学智能与信息系）；Department of Industrial Engineering, Seoul National University（首尔国立大学工业工程系）；LG AI Research（LG人工智能研究院）；LG Electronics（LG电子）；Google Research（谷歌研究院）
Phantom-Insight: Adaptive Multi-cue Fusion for Video Camouflaged Object Detection with Multimodal LLM
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection
Shanghai University（上海大学）；Agency for Science, Technology and Research（科技研究局）；University of Electronic Science and Technology of China（电子科技大学）；National University of Singapore（新加坡国立大学）
University of Chinese Academy of Sciences（中国科学院大学）；Sun Yat-Sen University（中山大学）；Tsinghua University（清华大学）
Camouflaged objects are seamlessly blended in with their surroundings, which brings a challenging detection task in computer vision. Optimizing a convolutional neural network (CNN) for camouflaged object detection (COD) tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue which inevitably leads to missing or redundant regions of objects. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN, where the convolution operations produce local receptive fields and experience difficulty t…
SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection
Computer Engineering Department, King Fahd University of Petroleum and Minerals（国王法赫德石油和矿物大学计算机工程系）；Department of Computer Science and Software Engineering, The University of Western Australia（西澳大学计算机科学与软件工程系）；Electronic Systems Engineering, University of Regina（里贾纳大学电子系统工程系）
C3Net: Context-Contrast Network for Camouflaged Object Detection
Computer Engineering Department, King Fahd University of Petroleum and Minerals（国王法赫德石油和矿物大学计算机工程系）；Electronic Systems Engineering, University of Regina（里贾纳大学电子系统工程系）；Department of Computer Science and Software Engineering, The University of Western Australia（西澳大学计算机科学与软件工程系）
DRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection
School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）；Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China（计算机网络与信息集成重点实验室（东南大学），教育部，中国）
Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception
Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China（未来媒体中心和电子科技大学计算机科学与工程学院）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；Peng Cheng Laboratory（鹏城实验室）
BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Key Laboratory of Fractional Signals and Systems（北京分数信号与系统重点实验室）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间生智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德雷塞尔大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程系，密苏里州立大学）
