Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections by utilizing initial masks generated by our custom-designed Cascaded Mask Decoder (CMD) to guide the segmentation process and enhance edge predictions. We introduce a Context Enhancement Module(CEM) to reduce the missing detection, and a Mask-guided Feature Aggregation Module (MFAM) for effective feature aggregation. For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels. By employing a redundant processing strategy, high quality pixel-level pseudo-labels are provided for training MGNet. Extensive experiments demonstrate that our method delivers competitive performance against current state-of-the-art methods.
How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results.…
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））
A Unified Query-based Paradigm for Camouflaged Instance Segmentation
Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）；G42；ETH Zurich（苏黎世联邦理工学院）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Jiangsu Provincial Key Laboratory of Computer Networking Technology（江苏省计算机网络技术重点实验室）；Jiangsu Provincial Key Laboratory of Network and Information Security（江苏省网络与信息安全重点实验室）；Key Laboratory of Computer Network and Information Integration of Ministry of Education of China（教育部计算机网络与信息集成重点实验室）；Nanjing Purple Mountain Laboratories（南京紫金山实验室）；Fintech and Big Data Laboratory of Southeast University（东南大学金融科技与大数据实验室）；Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（香港理工大学航空与航空工程系）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast（贝尔法斯特女王大学电子、电气与计算机科学学院）
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）
Monash University（莫纳什大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；ETH Zurich（苏黎世联邦理工学院）；SenseTime Research（商汤科技研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Data61, CSIRO（联邦科学与工业研究组织Data61）；Airdoc Research Australia（澳大利亚鹰瞳研究院）
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that they usually exhibit patterns similar to the background and thus make them hard to identify from still images. Therefore, effectively handling temporal dynamics in videos becomes the key for the VCOD task as the camouflaged objects will be noticeable when they move. However, current VCOD methods often leverage homography or optical flows to represent…
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）
Modality-Agnostic Prompt Learning for Multi-Modal Camouflaged Object Detection
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学与技术学院）；Beijing University of Technology（北京理工大学）；Dalian University of Technology（大连理工大学）
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）
Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset
School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；School of Electronic and Information Engineering, Guangdong Ocean University（广东海洋大学电子与信息工程学院）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；School of Computer Science, Nankai University（南开大学计算机科学学院）；Institute of Software, Chinese Academy of Science（中国科学院软件研究所）；School of Data Science, Lingnan University（岭南大学数据科学学院）
CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University（合成视觉基础科学国家重点实验室，四川大学）；College of Computer Science, Sichuan University（计算机学院，四川大学）
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…
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）
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…
ZS-VCOS: Zero-Shot Video Camouflaged Object Segmentation By Optical Flow and Open Vocabulary Object Detection
Department of CMPS, University of British Columbia（计算机科学与软件工程系，不列颠哥伦比亚大学）；Group of Methane Emission Observation & Warning (MEOW) , Weathon Software（甲烷排放观测与预警小组，Weathon软件）
We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS). OSFormer is based on two key designs. First, we design a location-sensing transformer (LST) to obtain the location label and instance-aware parameters by introducing the location-guided queries and the blend-convolution feedforward network. Second, we develop a coarse-to-fine fusion (CFF) to merge diverse context information from the LST encoder and CNN backbone. Coupling these two components enables OSFormer to efficiently blend local features and long-range context dependencies for pre…
Tencent Youtu Lab（腾讯优图实验室）；ETH Zurich（苏黎世联邦理工学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Sun Yat-sen University（中山大学）
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop…
SAM-TTT: Segment Anything Model via Reverse Parameter Configuration and Test-Time Training for Camouflaged Object Detection
College of Computer Science and Artificial Intelligence, Wenzhou University（计算机科学与人工智能学院，温州大学）；College of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；College of Information Science and Technology, Zhejiang Shuren University（信息科学与技术学院，浙江师大）
本文提出 SAM-TTT 模型，通过反向参数配置模块抑制不利参数，并引入 T-Visioner 模块利用测试时训练层强化有利参数，显著提升了 SAM 在伪装目标检测任务中的语义理解能力，并在多个基准上达到最先进性能。
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）
The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
University of Information Technology（信息科技大学）；Faculty of Computer Science（计算机学院）；Faculty of Information Technology（信息学院）；University of Science（科学大学）；John von Neumann Institute（约翰·冯·诺依曼研究所）；Vietnam National University（越南国家大学）；Monash University（莫纳什大学）；University of Dayton（代顿大学）
Distribution-Specific Learning for Joint Salient and Camouflaged Object Detection
School of Computing and Information Technology, Great Bay University（计算与信息学院，大湾大学）；Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT（计算机视觉与模式识别实验室，工程科学学院，拉佩兰塔-拉赫蒂技术大学）；School of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）；School of Software Engineering, Xi’an Jiaotong University（软件工程学院，西安交通大学）；School of Electrical and Information Engineering, Tianjin University（电气与信息工程学院，天津大学）
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）
High-Resolution Underwater Camouflaged Object Detection: GBU-UCOD Dataset and Topology-Aware and Frequency-Decoupled Networks
College of Shipbuilding Engineering, Harbin Engineering University（哈尔滨工程大学船舶工程学院）；School of Information Science and Technology, Great Bay University（广东工业大学信息科学与技术学院）
