Deep neural network based object detection hasbecome the cornerstone of many real-world applications. Alongwith this success comes concerns about its vulnerability tomalicious attacks. To gain more insight into this issue, we proposea contextual camouflage attack (CCA for short) algorithm to in-fluence the performance of object detectors. In this paper, we usean evolutionary search strategy and adversarial machine learningin interactions with a photo-realistic simulated environment tofind camouflage patterns that are effective over a huge varietyof object locations, camera poses, and lighting conditions. Theproposed camouflages are validated effective to most of the state-of-the-art object detectors.
GenCAMO: Scene-Graph Contextual Decoupling for Environment-aware and Mask-free Camouflage Image-Dense Annotation Generation
China University of Petroleum (East China)（中国石油大学（华东））；Southwest Jiaotong University（西南交通大学）；Beijing Information Science and Technology University（北京信息科技大学）；Beihang University（北航）
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…
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…
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）
Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
School of Computer Science and Artificial Intelligence, Changzhou University（常州大学计算机科学与人工智能学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；Department of Automation, Tsinghua University（清华大学自动化系）
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）
Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection
College of Artificial Intelligence, Nankai University, China（南开大学人工智能学院）；Department of Computing, The Hong Kong Polytechnic University, Hong Kong（香港理工大学计算机系）；School of Computer and Information, Anhui Normal University, China（安徽师范大学计算机与信息学院）
Assisted Refinement Network Based on Channel Information Interaction for Camouflaged and Salient Object Detection
Department of Physics, J.K. Institute of Science（物理系，J.K.科学研究院）；World Scientific University（世界科学大学）；University of Intelligent Studies（智能研究大学）；School of Computer Science and Technology, Xinjiang University（计算机科学与技术学院，新疆大学）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）
