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Dynamic interactive refinement network for camouflaged object detection

NEURAL COMPUTING & APPLICATIONS 2024年第7期36卷 3433-3446页

作者： Sun, Yaoqi Ma, Lidong Shou, Peiyao Wen, Hongfa Gao, Yuhan Liu, Yixiu Yan, Chenggang Yin, Haibing Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Lishui Inst Lishui 323000 Peoples R China

Automatically identifying objects similar to the surroundings is a complex and difficult task in real-world scenarios. In addition to the high intrinsic similarity between camouflaged objects and their backgrounds, these objects are usually diverse in scale and blurred in appearance. And the deceptive nature of the camouflaged objects introduces lots of noise into the features and generates inaccurate segmentation map extracted by deep learning model. We tackle these problems by proposing a novel dynamic interactive refinement network (DIRNet), which aims to make the features exploit effective details and semantics together as well as discard interference information. Specifically, we utilize bilateral interaction module (BIM) to interact with foreground and background information to conduct contextual exploration, which can capture more meaningful details and refine the confusion. Additionally, in the purpose of retaining the appropriate information and erasing noise, we design an adjacent aggregation interaction module (AAIM) to integrate the adjacent multi-level features with attention coefficients for each layer. The final results are obtained through the dynamic refinement of the BIM and AAIM. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that our proposed DIRNet is an effective COD framework and outperforms 14 state-of-the-art models.

关键词： camouflaged object detection Dynamic interactiverefinement network Adjacent aggregation interaction module Bilateral interaction module

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A Cross-Level Iterative Subtraction Network for camouflaged object detection

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APPLIED SCIENCES-BASEL 2024年第17期14卷 8063页

作者： Hu, Tongtong Zhang, Chao Lyu, Xin Sun, Xiaowen Chen, Shangjing Zeng, Tao Chen, Jiale Hohai Univ Coll Comp Sci & Software Engn Nanjing 211100 Peoples R China Minist Water Resources Informat Ctr Beijing 100053 Peoples R China Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 211100 Peoples R China Water Resources Serv Ctr Jiangsu Prov Nanjing 210029 Peoples R China

camouflaged object detection (COD) is a challenging task, aimed at segmenting objects that are similar in color and texture to their background. Sufficient multi-scale feature fusion is crucial for accurately segmenting object regions. However, most methods usually focus on information compensation, overlooking the difference between features, which is important for distinguishing the object from the background. To this end, we propose the cross-level iterative subtraction network (CISNet), which integrates information from cross-layer features and enhances details through iteration mechanisms. CISNet involves a cross-level iterative structure (CIS) for feature complementarity, where texture information is used to enrich high-level features and semantic information is used to enhance low-level features. In particular, we present a multi-scale strip convolution subtraction (MSCSub) module within CIS to extract difference information between cross-level features and fuse multi-scale features, which improves the feature representation and guides accurate segmentation. Furthermore, an enhanced guided attention (EGA) module is presented to refine features by deeply mining local context information and capturing a broader range of relationships between different feature maps in a top-down manner. Extensive experiments conducted on four benchmark datasets demonstrate that our model outperforms the state-of-the-art COD models in all evaluation metrics.

关键词： camouflaged object detection feature difference cross-level integration attention

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Edge Perception camouflaged object detection Under Frequency Domain Reconstruction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第10期34卷 10194-10207页

作者： Liu, Zijian Deng, Xiaoheng Jiang, Ping Lv, Conghao Min, Geyong Wang, Xin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Shenzhen Res Inst Shenzhen 518000 Peoples R China Cent South Univ Sch Elect Informat Changsha 410083 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Minist EducKey Lab Comp Power Network & Informat Jinan 250014 Peoples R China

camouflaged object detection has been considered a challenging task due to its inherent similarity and interference from background noise. It requires accurate identification of targets that blend seamlessly with the environment at the pixel level. Although existing methods have achieved considerable success, they still face two key problems. The first one is the difficulty in removing texture noise interference and thus obtaining accurate edge and frequency domain information, leading to poor performance when dealing with complex camouflage strategies. The latter is that the fusion of multiple information obtained from auxiliary subtasks is often insufficient, leading to the introduction of new noise. In order to solve the first problem, we propose a frequency domain reconstruction module based on contrast learning, through which we can obtain high-confidence frequency domain components, thus enhancing the model's ability to discriminate target objects. In addition, we design a frequency domain representation decoupling module for solving the second problem to align and fuse features from the RGB domain and the reconstructed frequency domain. This allows us to obtain accurate edge information while resisting noise interference. Experimental results show that our method outperforms 12 state-of-the-art methods in three benchmark camouflaged object detection datasets. In addition, our method shows excellent performance in other downstream tasks such as polyp segmentation, surface defect detection, and transparent object detection.

关键词： Frequency-domain analysis Image edge detection object detection Image reconstruction Semantics Noise Feature extraction camouflaged object detection salient object detection frequency domain reconstruction

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A survey on deep learning-based camouflaged object detection

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MULTIMEDIA SYSTEMS 2024年第5期30卷 1-23页

作者： Zhong, Junmin Wang, Anzhi Ren, Chunhong Wu, Jintao Guizhou Normal Univ Dept Big Data & Comp Sci Guiyang 550025 Peoples R China

camouflaged object detection (COD) is an emerging visual detection task that aims to identify objects that conceal themselves in the surrounding environment. The high intrinsic similarities between the camouflaged objects and their backgrounds make COD far more challenging than traditional object detection. Recently, COD has attracted increasing research interest in the computer vision community, and numerous deep learning-based methods have been proposed, showing great potential. However, most of the existing work focuses on analyzing the structure of COD models, with few overview works summarizing deep learning-based models. To address this gap, we provide a comprehensive analysis and summary of deep learning-based COD models. Specifically, we first classify 48 deep learning-based COD models and analyze their advantages and disadvantages. Second, we introduce widely available datasets for COD and performance evaluation metrics. Then, we evaluate the performance of existing deep learning-based COD models on these four datasets. Finally, we indicate relevant applications and discuss challenges and future research directions for the COD task.

关键词： camouflaged object detection Deep learning Attention mechanism Transformer

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MGL: Mutual Graph Learning for camouflaged object detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 1897-1910页

作者： Zhai, Qiang Li, Xin Yang, Fan Jiao, Zhicheng Luo, Ping Cheng, Hong Liu, Zicheng Univ Elect Sci & Technol China Ctr Robot Chengdu 611731 Peoples R China AIQ Abu Dhabi U Arab Emirates Brown Univ Radiol AI Lab Providence RI 02912 USA Univ Hong Kong Sch Comp Sci Hong Kong Peoples R China Microsoft AI Percept & Mixed Real Redmond WA 98101 USA

camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an effective model should be capable of finding valuable clues from the given scene and integrating them into a joint learning framework to co-enhance the representation. Inspired by this observation, we propose a novel Mutual Graph Learning (MGL) model by shifting the conventional perspective of mutual learning from regular grids to graph domain. Specifically, an image is decoupled by MGL into two task-specific feature maps - one for finding the rough location of the target and the other for capturing its accurate boundary details. Then, the mutual benefits can be fully exploited by reasoning their high-order relations through graphs recurrently. It should be noted that our method is different from most mutual learning models that model all between-task interactions with the use of a shared function. To increase information interactions, MGL is built with typed functions for dealing with different complementary relations. To overcome the accuracy loss caused by interpolation to higher resolution and the computational redundancy resulting from recurrent learning, the S-MGL is equipped with a multi-source attention contextual recovery module, called R-MGL_v2, which uses the pixel feature information iteratively. Experiments on challenging datasets, including CHAMELEON, CAMO, COD10K, and NC4K demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. The code can be found at https://***/fanyang587/MGL.

关键词： Graph learning camouflaged object detection

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Whole-body tumor segmentation from PET/CT images using a two-stage cascaded neural network with camouflaged object detection mechanisms

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MEDICAL PHYSICS 2023年第10期50卷 6151-6162页

作者： He, Jiangping Zhang, Yangjie Chung, Maggie Wang, Michael Wang, Kun Ma, Yan Ding, Xiaoyang Li, Qiang Pu, Yonglin Lanzhou Univ Finance & Econ Dept Elect Engn Lanzhou Gansu Peoples R China Univ Calif San Francisco Dept Radiol San Francisco CA USA Univ Calif San Francisco Dept Pathol San Francisco CA USA Univ Chicago Dept Radiol Chicago IL USA Univ Chicago Dept Radiol Chicago IL 60637 USA

BackgroundWhole-body Metabolic Tumor Volume (MTVwb) is an independent prognostic factor for overall survival in lung cancer patients. Automatic segmentation methods have been proposed for MTV calculation. Nevertheless, most of existing methods for patients with lung cancer only segment tumors in the thoracic region. PurposeIn this paper, we present a Two-Stage cascaded neural network integrated with camouflaged object detection mEchanisms (TS-Code-Net) for automatic segmenting tumors from whole-body PET/CT images. MethodsFirstly, tumors are detected from the Maximum Intensity Projection (MIP) images of PET/CT scans, and tumors' approximate localizations along z-axis are identified. Secondly, the segmentations are performed on PET/CT slices that contain tumors identified by the first step. camouflaged object detection mechanisms are utilized to distinguish the tumors from their surrounding regions that have similar Standard Uptake Values (SUV) and texture appearance. Finally, the TS-Code-Net is trained by minimizing the total loss that incorporates the segmentation accuracy loss and the class imbalance loss. ResultsThe performance of the TS-Code-Net is tested on a whole-body PET/CT image data-set including 480 Non-Small Cell Lung Cancer (NSCLC) patients with five-fold cross-validation using image segmentation metrics. Our method achieves 0.70, 0.76, and 0.70, for Dice, Sensitivity and Precision, respectively, which demonstrates the superiority of the TS-Code-Net over several existing methods related to metastatic lung cancer segmentation from whole-body PET/CT images. ConclusionsThe proposed TS-Code-Net is effective for whole-body tumor segmentation of PET/CT images. Codes for TS-Code-Net are available at: .

关键词： camouflaged object detection tumor segmentation two-stage cascaded neural network whole-body PET CT

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Toward Deeper Understanding of camouflaged object detection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第7期33卷 3462-3476页

作者： Lv, Yunqiu Zhang, Jing Dai, Yuchao Li, Aixuan Barnes, Nick Fan, Deng-Ping Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Australian Natl Univ Sch Comp Canberra ACT 2601 Australia Nankai Univ Comp Sci Dept Tianjin 300071 Peoples R China

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 conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the COD models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable COD network. Our code, data, and results are available at: https://***/JingZhang617/COD-Rank-Localize-and-Segment.

关键词： camouflaged object detection camouflaged object localization camouflage ranking

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Go Closer to See Better: camouflaged object detection via object Area Amplification and Figure-Ground Conversion

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第10期33卷 5444-5457页

作者： Xing, Haozhe Gao, Shuyong Wang, Yan Wei, Xujun Tang, Hao Zhang, Wenqiang Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Yiwu Res Inst Fudan Univ Yiwu 322000 Zhejiang Peoples R China

camouflaged object detection (COD) aims to detect objects well hidden in the environment. The main challenges of COD come from the high degree of texture and color overlapping between the objects and their surroundings. Inspired by that humans tend to go closer to the object and magnify it to recognize ambiguous objects more clearly, we propose a novel three-stage architecture called Search-Amplify-Recognize and design a network SARNet to address the challenges. Specifically, In the Search part, we utilize an attention-based backbone to locate the object. In the Amplify part, to obtain rich searched features and fine segmentation, we design object Area Amplification modules (OAA) to perform cross-level and adjacent-level feature fusion and amplifying operations on feature maps. Besides, the OAA can be regarded as a simple and effective plug-in module to integrate and amplify the feature maps. The main components of the Recognize part are the Figure-Ground Conversion modules (FGC). The FGC modules alternately pay attention to the foreground and background to precisely separate the highly similar foreground and background. Extensive experiments on benchmark datasets show that our model outperforms other SOTA methods not only on COD tasks but also in COD downstream tasks, such as polyp segmentation and video camouflaged object detection.

关键词： camouflaged object detection search-amplify-recognize architecture figure-ground conversion

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Zero-Shot camouflaged object detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 5126-5137页

作者： Li, Haoran Feng, Chun-Mei Xu, Yong Zhou, Tao Yao, Lina Chang, Xiaojun Univ Technol Sydney ReLER Lab AAII Ultimo NSW 2007 Australia Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia ASTAR Inst High Performance Comp IHPC Singapore 138632 Singapore Harbin Inst Technol Shenzhen Shenzhen Key Lab Visual Object Detect & Recognit Shenzhen 518055 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Univ New South Wales Sch Comp Sci & Engn Data Dynam Lab Sydney NSW 2052 Australia Univ Technol Sydney Fac Engn & Informat Technol ReLER Lab AAII Ultimo NSW 2007 Australia

The goal of camouflaged object detection (COD) is to detect objects that are visually embedded in their surroundings. Existing COD methods only focus on detecting camouflaged objects from seen classes, while they suffer from performance degradation to detect unseen classes. However, in a real-world scenario, collecting sufficient data for seen classes is extremely difficult and labeling them requires high professional skills, thereby making these COD methods not applicable. In this paper, we propose a new zero-shot COD framework (termed as ZSCOD), which can effectively detect the never unseen classes. Specifically, our framework includes a Dynamic Graph Searching Network (DGSNet) and a camouflaged Visual Reasoning Generator (CVRG). In details, DGSNet is proposed to adaptively capture more edge details for boosting the COD performance. CVRG is utilized to produce pseudo-features that are closer to the real features of the seen camouflaged objects, which can transfer knowledge from seen classes to unseen classes to help detect unseen objects. Besides, our graph reasoning is built on a dynamic searching strategy, which can pay more attention to the boundaries of objects for reducing the influences of background. More importantly, we construct the first zero-shot COD benchmark based on the COD10K dataset. Experimental results on public datasets show that our ZSCOD not only detects the camouflaged object of unseen classes but also achieves state-of-the-art performance in detecting seen classes.

关键词： camouflaged object detection zero-shot learn-ing graph representation

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Feature Aggregation and Propagation Network for camouflaged object detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 7036-7047页

作者： Zhou, Tao Zhou, Yi Gong, Chen Yang, Jian Zhang, Yu Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In PCA Lab Minist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Lehigh Univ Dept Bioengn Bethlehem 18015 PA USA

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 can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://***/taozh2017/FAPNet.

关键词： camouflaged object detection boundary guidance module multi-scale feature aggregation cross-level fusion feature propagation

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