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CoNet: A Consistency-Oriented Network for camouflaged object segmentation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第1期35卷 287-299页

作者： Wu, Fei Yin, Jun Li, Xiaochuan Wu, Jianfeng Jin, Da Yang, Jiamin Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China Zhejiang Univ Polytech Inst Zhejiang Hangzhou 310058 Peoples R China Dahua Technol Co Ltd Hangzhou 310053 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310058 Peoples R China

camouflaged object segmentation (COS) is a recently emerging task due to its broad application prospect. The coloration and texture similarities between the objects and their surroundings makes it a challenging task. Motivated by this, we propose a consistency-oriented network (CoNet) to address these challenges by looking into the visual consistencies between object and background. Specifically, we design a primary detection module (PDM) to firstly locate the object by fusing the backbone features. A filter is introduced to better focus on the object's foreground feature based on its primary location. To obtain the visual consistency between the object and background, the foreground feature is then fed into the consistency evaluation module (CEM) to interact with the global feature. Both features are simultaneously processed by a shared discriminator and then fused together to attain the consistency attention map. The final feature refinement is conducted in the detail refinement module (DRM) by merging the consistency attention map with the global features via hierarchical feature fusion. Extensive experiments on benchmark COS datasets show that the proposed CoNet outperforms the state-of-the-art (SOTA) models in most cases. Ablation experiments verify the effectiveness of different backbones, designed modules and upsampling methods. Furthermore, extra studies on the labelling techniques and interdisciplinary applications demonstrate the great potential of the proposed CoNet.

关键词： Feature extraction Visualization object segmentation object detection Estimation object recognition Image resolution camouflaged object detection camouflaged object segmentation consistency-oriented global-foreground consistency

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Curiosity-Driven camouflaged object segmentation

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APPLIED SCIENCES-BASEL 2025年第1期15卷 173-173页

作者： Pang, Mengyin Sun, Meijun Wang, Zheng Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Tianjin Key Lab Machine Learning Tianjin 300350 Peoples R China

camouflaged object segmentation refers to the task of accurately extracting objects that are seamlessly integrated within their surrounding environment. Existing deep-learning methods frequently encounter challenges in accurately segmenting camouflaged objects, particularly in capturing their complete and intricate details. To this end, we propose a novel method based on the Curiosity-Driven network, which is motivated by the innate human tendency for curiosity when encountering ambiguous regions and the subsequent drive to explore and observe objects' details. Specifically, the proposed fusion bridge module aims to exploit the model's inherent curiosity to fuse these features extracted by the dual-branch feature encoder to capture the complete details of the object. Then, drawing inspiration from curiosity, the curiosity-refinement module is proposed to progressively refine the initial predictions by exploring unknown regions within the object's surrounding environment. Notably, we develop a novel curiosity-calculation operation to discover and remove curiosity, leading to accurate segmentation results. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing competitors on three challenging benchmark datasets. Compared with the recently proposed state-of-the-art method, our model achieves performance gains of 1.80% on average for S alpha. Moreover, our model can be extended to the polyp and industrial defects segmentation tasks, validating its robustness and effectiveness.

关键词： deep-learning camouflaged object segmentation curiosity-driven

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MirrorNet: Bio-Inspired camouflaged object segmentation

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IEEE ACCESS 2021年 9卷 43290-43300页

作者： Yan, Jinnan Trung-Nghia Le Khanh-Duy Nguyen Minh-Triet Tran Thanh-Toan Do Nguyen, Tam, V Univ Dayton Dept Comp Sci Dayton OH 45469 USA Natl Inst Informat Tokyo 1018430 Japan Vietnam Natl Univ Univ Informat Technol Multimedia Commun Lab Ho Chi Minh City 7000060 Vietnam Vietnam Natl Univ Univ Sci Fac Informat Technol Ho Chi Minh City 7000060 Vietnam Monash Univ Fac Informat Technol Dept Data Sci & AI Clayton Vic 3800 Australia

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 bio-inspired attack stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the bio-inspired attack stream corresponding with the original image and its flipped image, respectively. The output from the bio-inspired attack stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts.

关键词： Streaming media object segmentation Image segmentation Image color analysis Biomedical imaging Training object recognition camouflaged object segmentation bio-inspired network

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Anabranch network for camouflaged object segmentation

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COMPUTER VISION AND IMAGE UNDERSTANDING 2019年 184卷 45-56页

作者： Trung-Nghia Le Nguyen, Tam, V Nie, Zhongliang Minh-Triet Tran Sugimoto, Akihiro Grad Univ Adv Studies SOKENDAI Dept Informat Tokyo Japan Univ Dayton Dept Comp Sci Dayton OH 45469 USA Univ Sci VNU HCM Ho Chi Minh Vietnam Natl Inst Informat Tokyo Japan Univ Tokyo Inst Ind Sci Tokyo Japan

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. This paper addresses a new challenging problem of camouflaged object segmentation. To address this problem, we provide a new image dataset of camouflaged objects for benchmarking purposes. In addition, we propose a general end-to-end network, called the Anabranch Network, that leverages both classification and segmentation tasks. Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy. Extensive experiments conducted on the newly built dataset demonstrate the effectiveness of our network using various fully convolutional networks.

关键词： camouflaged object segmentation Anabranch network

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camouflaged object segmentation with prior via two-stage training

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COMPUTER VISION AND IMAGE UNDERSTANDING 2024年 246卷

作者： Wang, Rui Shi, Caijuan Duan, Changyu Gao, Weixiang Zhu, Hongli Wei, Yunchao Liu, Meiqin North China Univ Sci & Technol Dept Artificial Intelligence Tangshan 063210 Peoples R China Hebei Key Lab Ind Intelligent Percept Tangshan 063210 Peoples R China Beijing Key Lab Modern Informat Sci & Network Tech Beijing 100044 Peoples R China

The camouflaged object segmentation (COS) task aims to segment objects visually embedded within the background. Existing models usually rely on prior information as an auxiliary means to identify camouflaged objects. However, low-quality priors and the singular guidance form hinder the effective utilization of prior information. To address these issues, we propose a novel approach for prior generation and guidance, named prior-guided transformer (PGT). For prior generation, we design a prior generation subnetwork consisting of a Transformer backbone and simple convolutions to obtain higher-quality priors at a lower cost. In addition, to fully exploit the backbone's understanding capabilities of the camouflage characteristics, a novel two-stage training method is proposed to achieve the backbone's deep supervision. For prior guidance, we design a prior guidance modules (PGM), with distinct space token mixers to respectively capture global dependencies of location priors and local details of boundary priors. Additionally, we introduce a cross-level prior in the form of features to facilitate inter-level communication of backbone features. Extensive experiments have been conducted and experimental results illustrate the effectiveness and superiority of our method. The code is available at https://***/Ray3417/PGT.

关键词： camouflaged object segmentation Transformer Prior information Two-stage training

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Few-Shot camouflaged object segmentation

Few-Shot Camouflaged Object Segmentation

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Ziqiu Li, Yuying Yang, Yang Lie, Yamin Liu, Gaoyang Hubei Univ Sch Artificial Intelligence Key Lab Intelligent Sensing Syst & Secur Minist Educ Wuhan Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350359329;9798350359312

In the domain of computer vision, camouflaged object segmentation (COS) is a crucial task aimed at identifying objects that blend into their surroundings, with applications spanning diverse sectors such as military, medical, and beyond. Traditional COS techniques, which primarily depend on supervised learning, necessitate large-scale labeled datasets. However, the acquisition of sufficient camouflage images for such purposes is often constrained due to their scarcity and the high cost of manual annotation. Additionally, these conventional methods frequently struggle to generalize to novel, unseen classes. In response to these challenges, this paper proposes the Camouflage FewShot (CAMFS) framework, an innovative approach integrating few-shot learning into COS. The CAMFS framework comprises two main components: the camouflaged-Meta module, which converts the semantic information of camouflaged objects into compact feature vectors to facilitate knowledge transfer from support to query images;and the camouflaged-Base module, focused on refining edge detection and enhancing the contrast between foreground and background elements. To overcome the limitations of existing COS datasets, which are primarily designed for supervised learning, we have developed the COSFSS dataset, the first public few-shot COS dataset. It is based on the COD10K dataset and supplemented with approximately 3200 additional camouflage images. We conducted extensive evaluations of our CAMFS framework on the COS-FSS dataset. Compared to existing COS models, CAMFS demonstrates an average improvement of 5.7% in Sa and 14.02% in F w ss, while against few-shot segmentation models, it achieves a 5.97% increase in m-IoU. The dataset and additional resources are available at https://***/CAM-FSS/FSS-COD.

关键词： camouflaged object segmentation Few-Shot Learning Computer Vision

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BiDiCOS: camouflaged object segmentation via bilateral diffusion model

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Jiang, Xinhao Cai, Wei Ding, Yao Wang, Xin Hong, Danfeng Di, Xingyu Gao, Weijie PLA Rocket Force Univ Engn Key Lab Opt Engn Xian 710025 Peoples R China High tech Inst Fan Gong-ting South St 12th Qing Zhou Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China

The task of camouflaged object segmentation (COS) is to extract objects with camouflage characteristics that are difficult to detect in an image. The current mainstream COS method mainly relies on the general object segmentation framework using the encoder-decoder structure to predict the objects. However, the current COS framework suffers from over-reliance on the labeled ground truth and the output results. To this end, we propose a bilateral diffusion model for camouflaged object segmentation (BiDiCOS). The main innovation of BiDiCOS is that we design a new bilateral diffusion paradigm, transforming the segmentation and depth estimation tasks into denoising tasks. At the same time, the depth estimation branch can provide semantic information to the segmentation branch. Specifically, we design the adaptive feature fusion module for two branches to provide the required features adaptively. We design the bilateral feature fusion module to fuse the depth information with the mask information to reduce the semantic and logical errors during segmentation. Finally, we develop the specialized training strategy and inference strategy for BiDiCOS, which provide a solid foundation for realizing rational training and accurate prediction. Comprehensive experiments on four challenging COS datasets attest to the superior performance of BiDiCOS compared to the existing 20 COS methods. Our BiDiCOS achieves state-ofthe-art with different sizes of backbones, where the MAE is reduced by 4.3 % on average. We have also performed comparative experiments on depth estimation. The experiments demonstrate that BiDiCOS can achieve a win-win situation for both tasks. The code will be available at https://***/jiangxinhao2020/BiDiCOS.

关键词： camouflaged object segmentation Diffusion model Depth estimation Computer vision

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Improving underwater camouflage object segmentation with dual-decoder attention network

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-21页

作者： Liu, Yiwen Zhang, Xiaoyu Zhu, Jinchao Tan, Panlong Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Inst Robot & Automat Informat Syst Tianjin 300350 Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100089 Peoples R China

Underwater camouflaged object segmentation is to segment the camouflaged object from the background in underwater images. Compared to generic objects, camouflaged objects are difficult to segment due to their high similarity with the surrounding environment. This paper presents a new convolutional neural network called Dual-Decoder Attention Network (DDANet) to address this problem. Inspired by the visual process of identifying camouflaged objects, DDANet adopts the design of dual-decoder, introduces non-local attention mechanisms, and optimizes structures such as global and edge guidance bypass to achieve better segmentation performance. The experiments demonstrate that DDANet outperforms select relevant methods in multiple metrics on different camouflaged object datasets. The paper also introduces a new composite underwater camouflage object dataset called Aqua-Test and reports DDANet's competitive performance on this dataset. These results demonstrate the effectiveness of DDANet in identifying underwater camouflage objects.

关键词： camouflaged object segmentation Underwater camouflaged object segmentation Non-local attention Dual-decoder

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