Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show visually distinct characteristics and thus the foreground can be detected once a good background model is obtained. However, there are many situations where this is not the case. Of particular interest in video surveillance is the camouflage case. For example, an active attacker camouflages by intentionally wearing clothes that are visually similar to the background. In such cases, even given a decent background model, it is not trivial to detect foreground objects. This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes. The proposed method employs the stationary wavelet transform to decompose the image into frequency bands. We show that the small and hardly noticeable differences between foreground and background in the image domain can be effectively captured in certain wavelet frequency bands. To make the final foreground decision, a weighted voting scheme is developed based on intensity and texture of all the wavelet bands with weights carefully designed. Experimental results demonstrate that the proposed method achieves superior performance compared to the current state-of-the-art results.
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Jilin University（吉林大学）；MediaTek（联发科）；National Taiwan University（国立台湾大学）
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…
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）
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）
RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance
College of Artificial Intelligence, Nankai University, Tianjin, China（人工智能学院，南开大学，天津，中国）；School of Data Science, Fudan University, Shanghai, China（数据科学学院，复旦大学，上海，中国）
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）
Certainty Is Redundant: Token Sparsification for Efficient Camouflaged Object Detection with Vision Foundation Models
VCIP, College of Computer Science, Nankai University（VCIP，计算机科学学院，南开大学）；Academy for Advanced Interdisciplinary Studies, Nankai University（先进交叉学科研究院，南开大学）；School of Computer Science and Engineering, Tianjin University of Technology（计算机科学与工程学院，天津工业大学）；School of Information and Communication Engineering, UESTC（信息与通信工程学院，电子科技大学）；Nankai International Advanced Research Institute, Shenzhen Futian（南开国际先进研究院，深圳福田）
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…
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 …
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection
Department of Electrical and Computer Engineering, Northeastern University（电气与计算机工程系，东北大学）；Department of Electrical Engineering and Computer Science, Case Western Reserve University（电气工程与计算机科学系，凯斯西储大学）
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）
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）
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）
UESTC（电子科技大学）；Group 42 (G42)（G42集团）；Qingdao University（青岛大学）；Inception Institute of AI (IIAI)（Inception人工智能研究院）
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an ideal model should be able to seek valuable, extra clues from the given scene and incorporate them into a joint learning framework for representation co-enhancement. With this inspiration, we design a novel Mutual Graph Learning (MGL) model, which generalizes the idea of convent…
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）
Explicit Motion Handling and Interactive Prompting 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（计算机学院）；School of Computing（计算学院）；Australian National University（澳大利亚国立大学）
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…
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis. However, without considering the foreground-background decomposition, existing GANs tend to capture excessive content correlation between foreground and background, thus constraining the diversity in image generation. This paper presents a novel Foreground-Background Composition GAN (FBC-GAN) that performs image generation by generating foreground objects and background scenes concurrently and independently, followed by composing them with style and geometrical consistency. With this explicit design, F…
Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
ESPOL Polytechnic University（ESPOL理工大学）；Computer Vision Center, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；Software Engineering Department, Research Center for Information and Communication Technologies (CITIC-UGR), University of Granada（格拉纳达大学软件工程系，信息与通信技术研究中心（CITIC-UGR））；Università della Svizzera Italiana（瑞士意大利大学）
Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally varying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the foreground component using l1-norm, and minimizes the modeling error via 2-norm. We show that such a…
Image segmentation in the urban scene has recently attracted much attention due to its success in autonomous driving systems. However, the poor performance of concerned foreground targets, e.g., traffic lights and poles, still limits its further practical applications. In urban scenes, foreground targets are always concealed in their surrounding stuff because of the special camera position and 3D perspective projection. What's worse, it exacerbates the unbalance between foreground and background classes in high-level features due to the continuous expansion of the reception field. We call it F…
Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. O…
University of Copenhagen（哥本哈根大学）；Technical University of Denmark（丹麦技术大学）
Recently, advances in differential volumetric rendering enabled significant breakthroughs in the photo-realistic and fine-detailed reconstruction of complex 3D scenes, which is key for many virtual reality applications. However, in the context of augmented reality, one may also wish to effect semantic manipulations or augmentations of objects within a scene. To this end, we propose a volumetric framework for (i) disentangling or separating, the volumetric representation of a given foreground object from the background, and (ii) semantically manipulating the foreground object, as well as the ba…
A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；School of Computing and Information Technology, Great Bay University（湾区大学计算与信息技术学院）；Computer Vision and Pattern Recognition Laboratory, Lappeenranta-Lahti University of Technology LUT（拉彭兰塔-拉赫蒂理工大学计算机视觉与模式识别实验室）；School of Statistics and Data Science, Nankai University（南开大学统计与数据科学学院）
本文提出基于 ViT 的简单非对称编解码网络 SENet，结合 LICM 与动态加权损失，并解决 COD/SOD 联合训练冲突，在两类检测任务上均取得有竞争力的性能。
Previous methods for representing scene images based on deep learning primarily consider either the foreground or background information as the discriminating clues for the classification task. However, scene images also require additional information (hybrid) to cope with the inter-class similarity and intra-class variation problems. In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images. We suppose that these three types of information could jointly help to represent scene image more accurately. To this end, we adopt three…
Scene-Adaptive Person Search via Bilateral Modulations
School of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Cyber Security and Computer, Hebei University（河北大学网络空间安全与计算机学院）；Hefei University of Technology（合肥工业大学）
