Current zero-shot Camouflaged Object Segmentation methods typically employ a two-stage pipeline (discover-then-segment): using MLLMs to obtain visual prompts, followed by SAM segmentation. However, relying solely on MLLMs for camouflaged object discovery often leads to inaccurate localization, false positives, and missed detections. To address these issues, we propose the \textbf{D}iscover-\textbf{S}egment-\textbf{S}elect (\textbf{DSS}) mechanism, a progressive framework designed to refine segmentation step by step. The proposed method contains a Feature-coherent Object Discovery (FOD) module that leverages visual features to generate diverse object proposals, a segmentation module that refines these proposals through SAM segmentation, and a Semantic-driven Mask Selection (SMS) module that employs MLLMs to evaluate and select the optimal segmentation mask from multiple candidates. Without requiring any training or supervision, DSS achieves state-of-the-art performance on multiple COS benchmarks, especially in multiple-instance scenes.
ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）
The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while ma…
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.…
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…
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…
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）
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 …
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）
Dalian University of Technology（大连理工大学）；Wuhan University（武汉大学）；Tsinghua University（清华大学）；IIAI
Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning modul…
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）
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…
University of Dayton（代顿大学）；Institute of Industrial Science, the University of Tokyo（东京大学生产技术研究所）；The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学）；University of Science, VNU-HCM（越南国立大学胡志明市分校科学大学）；National Institute of Informatics（国立信息学研究所）
Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has not been well studied in spite of a wide range of potential applications including the preservation of wild animals and the discovery of new species, surveillance systems, search-and-rescue missions in the event of natural disasters such as earthquakes, floods or hurricanes. Thi…
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））
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）
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）
SPEGNet: Synergistic Perception-Guided 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）；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）；Electronic Systems Engineering, University of Regina（里贾纳大学电子系统工程系）
University of Chinese Academy of Sciences（中国科学院大学）；Duke University（杜克大学）；Beihang University（北京航空航天大学）；Oulu University（奥卢大学）
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. The self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedure, object locations in each frame are treated as latent variables that are solved with a progressive latent model (PLM). Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization,…
Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentation methods for individual architectures. In this work, we present the first attempt to automate the design of data augmentation policies for 3D object detection. We introduce the Progressive Population Based Augmentation (PPBA) algorithm, which learns to optimize augmentation strategies by narrowing down the search space and adopting the best parameters discovered in previous iterations. On the KITTI 3D detection tes…
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Huazhong University of Science and Technology（华中科技大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
National University of Singapore（新加坡国立大学）；CMU（卡内基梅隆大学）；Nankai University（南开大学）；Beijing Jiaotong University（北京交通大学）；AI Institute（360人工智能研究院）
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems. Classification networks are only responsive to small and sparse discriminative regions from the object of interest, which deviates from the requirement of the segmentation task that needs to localize dense, interior and integral regions for pixel-wise inference. To mitigate this gap, we propose a new adversarial erasing approach for localizing and expanding object regions progressively. Starting with a single small obj…
What mechanisms causes GAN's entanglement? Although developing disentangled GAN has attracted sufficient attention, it is unclear how entanglement is originated by GAN transformation. We in this research propose a difference-in-difference (DID) counterfactual framework to design experiments for analyzing the entanglement mechanism in on of the Progressive-growing GAN (PG-GAN). Our experiment clarify the mechanisms how pixel normalization causes PG-GAN entanglement during a input-unit-ablation transformation. We discover that pixel normalization causes object entanglement by in-painting the are…
University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）；University of California San Diego（加州大学圣迭戈分校）；RMIT University（皇家墨尔本理工大学）
Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations. A promising strategy is to learn a global-local representation that incorporates global information with extra localities (i.e., small parts/regions of inputs). However, existing methods discover localities based on explicit features without digging into the inherent properties and relationships among regions. In this work, we propose a novel Entropy-guided Reinforced Partial Convolutional Network (ERPCNet), which extracts and aggregates localities progressively based …
