Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods.
Northwestern Polytechnical University（西北工业大学）；Australian National University（澳大利亚国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；Inception Institute of AI (IIAI)（Inception人工智能研究院）
Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [35]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged ob…
Learning Camouflaged Object Detection from Noisy Pseudo Label
Beijing Institute of Technology（北京理工大学）；Shanghai Institute of Technology（上海应用技术大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Linköping University（林雪平大学）
A Survey of Camouflaged Object Detection and Beyond
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Peking University（北京大学）；School of Electrical and Computer Engineering, Peking University（北京大学电气与计算机工程学院）；Duke University（杜克大学）
Northwestern Polytechnical University（西北工业大学）；Xidian University（西安电子科技大学）；the Australian National University（澳大利亚国立大学）；Nankai University（南开大学）
Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the…
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）
Just a Hint: Point-Supervised Camouflaged Object Detection
Unmanned System Research Institute, Northwestern Polytechnical University（西北工业大学无人系统研究院）；National Key Laboratory of Unmanned Aerial Vehicle Technology（无人机技术国家重点实验室）
Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes
School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学院）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）
The University of Tokyo（东京大学）；National Institute of Informatics（国立信息学研究所）
This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target object directly while outputting ambiguous camouflaged boundaries. On the other hand, the performance of the models considering edge information is not yet satisfactory. To this end, we propose a new framework that makes full use of multiple visual cues, i.e., saliency as well as edges, to refine the predicted camouflaged map. This framework consists of three key components, i.e., a pseudo-edge generator, a pseudo-map g…
School of Computer Science, Nankai University（南开大学计算机学院）；BNRist, Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系BNRist（注：BNRist为北京信息科学与技术国家研究中心的英文缩写））
Northwestern Polytechnical University（西北工业大学）；Australian National University（澳大利亚国立大学）；SenseTime（商汤科技）
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings. Research in biology suggests depth can provide useful object localization cues for camouflaged object discovery. In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods. Due to the domain gap between the MDE dataset and our COD dataset, the generated depth maps are not accurate enough to …
Nanjing University of Science and Technology（南京理工大学）；Southeast University（东南大学）；Lehigh University（理海大学）
Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which …
Inner Mongolia University（内蒙古大学）；Nanjing University of Science and Technology（南京理工大学）；Institut National des Sciences Appliquées de Rennes（雷恩国立应用科学学院）；IIAI
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the pr…
Yonsei University（延世大学）；Korea Institute of Science and Technology (KIST)（韩国科学技术院）
The camouflaged object detection (COD) task aims to identify and segment objects that blend into the background due to their similar color or texture. Despite the inherent difficulties of the task, COD has gained considerable attention in several fields, such as medicine, life-saving, and anti-military fields. In this paper, we propose a novel solution called the Deformable Point Sampling network (DPS-Net) to address the challenges associated with COD. The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary info…
Existing camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-wise annotations. However, due to the ambiguous boundary, annotating camouflage objects pixel-wisely is very time-consuming and labor-intensive, taking ~60mins to label one image. In this paper, we propose the first weakly-supervised COD method, using scribble annotations as supervision. To achieve this, we first relabel 4,040 images in existing camouflaged object datasets with scribbles, which takes ~10s to label one image. As scribble annotations only describe the primary structure of objects …
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）
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
School of Computer Science, Wuhan University（武汉大学计算机学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Computer of Science, China University of Geosciences, China（中国地质大学计算机学院）；College of Computer Science, Sichuan University（四川大学计算机学院）
Northwestern Polytechnical University（西北工业大学）；Australian National University（澳大利亚国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；EPFL（瑞士洛桑联邦理工学院）
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding. In this paper, we propose a paradigm of leveraging the contradictory information to enhance the detection ability of both salient object detection and camouflaged object detection. We start by exploiting the easy positive samples in the COD dataset to serve as hard positive samples in the SOD task to improve the robustness of the SOD model. Then, we introdu…
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）
Huazhong University of Science and Technology（华中科技大学）；ETH Zürich（苏黎世联邦理工学院）；Hainan University（海南大学）；Hubei Chutian Expressway Digital Technology（湖北楚天高速数字科技有限公司）
Recent research about camouflaged object detection (COD) aims to segment highly concealed objects hidden in complex surroundings. The tiny, fuzzy camouflaged objects result in visually indistinguishable properties. However, current single-view COD detectors are sensitive to background distractors. Therefore, blurred boundaries and variable shapes of the camouflaged objects are challenging to be fully captured with a single-view detector. To overcome these obstacles, we propose a behavior-inspired framework, called Multi-view Feature Fusion Network (MFFN), which mimics the human behaviors of fi…
SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）
EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
West China Hospital, Sichuan University（四川大学华西医院）；Independent Researcher（独立研究员）；Department of Data Science and Artificial Intelligence, Auckland University of Technology（奥克兰理工大学数据科学与人工智能系）
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Computer Science & Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Intelligent Science and Technology Academy of CASIC（中国航天科工集团有限公司智能科技研究院）
Northwestern Polytechnical University（西北工业大学）；Nanjing University of Science and Technology（南京理工大学）；Inner Mongolia University（内蒙古大学）；Wuhan University（武汉大学）
Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Mod…
Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection
School of Computer Science (National Pilot Software Engineering School), Beijing University Of Posts and Telecommunications（北京邮电大学计算机学院（国家示范性软件学院））；Inception Institute of Artificial Intelligence（Inception人工智能研究院）；G42 Bayanat（G42巴亚纳特公司）；School of Software, Beihang University（北京航空航天大学软件学院）；School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机学院）；Center for Brain Imaging Science and Technology, Zhejiang University（浙江大学）；School of Computer Science, China University of Geosciences（中国地质大学计算机学院）；Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology（上海理工大学）；School of Electrical Engineering & Information Department, Northeast Petroleum University（东北石油大学）；School of Electronic, Electrical Engineering and Physics, Fujian University of Technology（福建工程学院）
