Due to the high cost of collection and labeling, there are relatively few datasets for camouflaged object detection (COD). In particular, for certain specialized categories, the available image dataset is insufficiently populated. Synthetic datasets can be utilized to alleviate the problem of limited data to some extent. However, directly training with synthetic datasets compared to real datasets can lead to a degradation in model performance. To tackle this problem, in this work, we investigate a new task, namely Syn-to-Real Camouflaged Object Detection (S2R-COD). In order to improve the model performance in real world scenarios, a set of annotated synthetic camouflaged images and a limited number of unannotated real images must be utilized. We propose the Cycling Syn-to-Real Domain Adaptation Framework (CSRDA), a method based on the student-teacher model. Specially, CSRDA propagates class information from the labeled source domain to the unlabeled target domain through pseudo labeling combined with consistency regularization. Considering that narrowing the intra-domain gap can improve the quality of pseudo labeling, CSRDA utilizes a recurrent learning framework to build an evolving real domain for bridging the source and target domain. Extensive experiments demonstrate the effectiveness of our framework, mitigating the problem of limited data and handcraft annotations in COD. Our code is publicly available at: https://github.com/Muscape/S2R-COD.
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 …
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…
South China University of Technology（华南理工大学）；The Chinese University of Hong Kong（香港中文大学）；The Open University of Hong Kong（香港公开大学）
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. The texture-aware refinement module computes the covariance matrices of feature responses to extract the texture information, designs an affinity loss to learn a set of parameter maps that help to sepa…
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and several essential survey papers exist. However, the research on camouflage object detection and tracking limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this article, we review the existing camouflaged object detection and tracking techniques using compu…
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.…
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）
Camouflaged object detection intends to discover the concealed objects hidden in the surroundings. Existing methods follow the bio-inspired framework, which first locates the object and second refines the boundary. We argue that the discovery of camouflaged objects depends on the recurrent search for the object and the boundary. The recurrent processing makes the human tired and helpless, but it is just the advantage of the transformer with global search ability. Therefore, a dual-task interactive transformer is proposed to detect both accurate position of the camouflaged object and its detail…
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…
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）；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…
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection
University of Electronic Science and Technology of China（电子科技大学）；Group 42 (G42)（G42集团）；Sichuan Agricultural University（四川农业大学）；Southwest Jiaotong University（西南交通大学）；Brown University（布朗大学）
CAMotion: A High-Quality Benchmark for Camouflaged Moving Object Detection in the Wild
School of CyberScience and Technology, Sun Yat-sen University（中山大学网络空间安全学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；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））
Confidence-aware learning is proven as an effective solution to prevent networks becoming overconfident. We present a confidence-aware camouflaged object detection framework using dynamic supervision to produce both accurate camouflage map and meaningful "confidence" representing model awareness about the current prediction. A camouflaged object detection network is designed to produce our camouflage prediction. Then, we concatenate it with the input image and feed it to the confidence estimation network to produce an one channel confidence map.We generate dynamic supervision for the confidenc…
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…
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…
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 …
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）
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 …
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）
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）
Referring Camouflaged Object Detection With Multi-Context Overlapped Windows Cross-Attention
School of Computer and Information Engineering, Shanghai Polytechnic University（计算机与信息工程学院，上海理工大学）；Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University（智能信息处理重点实验室，复旦大学）；Department of Automation, School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University（自动化系，智能制造与控制工程学院，上海理工大学）；Academy for Engineering and Technology, Fudan University（工程与技术学院，复旦大学）；Research and Design Center, Shanghai Insititue Of Computer Technology Company（研发设计中心，上海计算机技术有限公司）
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…
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）
Sichuan Changhong Electric Co., Ltd.（四川长虹电器股份有限公司）；UESTC（电子科技大学）；University of Glasgow（格拉斯哥大学）；G42（G42公司）；ETH Zurich（苏黎世联邦理工学院）；CCST, NUAA（南京航空航天大学计算机科学与技术学院）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrin…
University of Dayton（代顿大学）；National Institute of Informatics（信息学研究所）；Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）；University of Information Technology, VNU-HCM（胡志明市越南国家大学信息技术大学）；University of Science, Ho Chi Minh City, Vietnam（越南胡志明市科学大学）；John von Neumann Institute, VNU-HCM（胡志明市越南国家大学约翰·冯·诺依曼研究所）；Monash University（莫纳什大学）
Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the fi…
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…
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…
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）
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）
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…
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）
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…
