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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

Overview of the CCKS 2019 knowledge graph evaluation track: Entity, relation, event and QA

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arXiv 2020年

作者： Han, Xianpei Wang, Zhichun Zhang, Jiangtao Wen, Qinghua Li, Wenqi Tang, Buzhou Wang, Qi Feng, Zhifan Zhang, Yang Lu, Yajuan Wang, Haitao Chen, Wenliang Shao, Hao Chen, Yubo Liu, Kang Zhao, Jun Wang, Taifeng Zhang, Kezun Wang, Meng Jiang, Yinlin Qi, Guilin Zou, Lei Hu, Sen Zhang, Minhao Lin, Yinnian Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence Beijing Normal University Beijing100875 China 305 Hospital of People’s Liberation Army Beijing100017 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Yidu Cloud Beijing100000 China Harbin Institute of Technology Shenzhen Guangdong518055 China Baidu Beijing100193 China School of Computer Science & Technology Soochow University Suzhou Jiangsu215006 China Gowild Suzhou Jiangsu215002 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Ant Financial Hangzhou Zhejiang310000 China School of Computer Science and Engineering Southeast University Nanjing Jiangsu211189 China Key Laboratory of Computer Network Information Integration Southeast University Nanjing Jiangsu211189 China Peking University Beijing100871 China

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

Denoising of 3D magnetic resonance images using a residual encoder-decoder wasserstein generative adversarial network

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arXiv 2018年

作者： Ran, Maosong Hu, Jinrong Chen, Yang Chen, Hu Sun, Huaiqiang Zhou, Jiliu Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Department of Computer Science Chengdu University of Information Technology Chengdu610225 China Lab of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China Ministry of Education Nanjing210096 China Department of Radiology West China Hospital of Sichuan University Chengdu610041 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions

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arXiv 2023年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Spitale, Micol Ge, Zongyuan Shen, Linlin Gunes, Hatice Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Computer Sciences University of Exeter ExeterEX4 4PY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Airdoc-Monash Research Centre Monash University Faculty of IT Monash University Melbourne Australia

In dyadic interaction, predicting the listener’s facial reactions is challenging as different reactions could be appropriate in response to the same speaker’s behaviour. Previous approaches predominantly treated this task as an interpolation or fitting problem, emphasizing deterministic outcomes but ignoring the diversity and uncertainty of human facial reactions. Furthermore, these methods often failed to model short-range and long-range dependencies within the interaction context, leading to issues in the synchrony and appropriateness of the generated facial reactions. To address these limitations, this paper reformulates the task as an extrapolation or prediction problem, and proposes an novel framework (called ReactFace) to generate multiple different but appropriate facial reactions from a speaker behaviour rather than merely replicating the corresponding listener facial behaviours. Our ReactFace generates multiple different but appropriate photo-realistic human facial reactions by: (i) learning an appropriate facial reaction distribution representing multiple different but appropriate facial reactions;and (ii) synchronizing the generated facial reactions with the speaker verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences. Experimental results demonstrate the effectiveness of our approach in generating multiple diverse, synchronized, and appropriate facial reactions from each speaker’s behaviour. The quality of the generated facial reactions is intimately tied to the speaker’s speech and facial expressions, achieved through our novel speaker-listener interaction modules. Our code is made publicly available at https://***/lingjivoo/ReactFace. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

An Improved Epsilon Constraint Handling Method Embedded in MOEA/D for Constrained Multi-objective Optimization Problems

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An Improved Epsilon Constraint Handling Method Embedded in M...

IEEE Symposium Series on Computational Intelligence

作者： Zhun Fan Wenji Li Xinye Cai Hui Li Han Huang Zhaoquan Cai Caimin Wei Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Department of Electronic Engineering Shantou University 515063 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi 710049 China Department of Mathematics Shantou University Guangdong 515063 China Department of Electronic Engineering Shantou University Guangdong 515063 China School of Software Engineering South China University of Technology Guangdong Guangzhou 510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu 210016 China Department of Computer Science Huizhou University Guangdong 516007 China

ISBN: (纸本)9781509042418

This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critical parameter in the epsilon constraint method, according to the feasible ratio of solutions in the current population. In order to verify the effect of the improved epsilon constraint handling method, three algorithms - MOEA/D-CDP, MOEA/D-Epsilon, and MOEA/D-IEpsilon (MOEA/D with the improved epsilon constraint handling mechanism) are tested on nine CMOPs (CMOP1-CMOP9). The comprehensive experimental results indicate that the proposed epsilon constraint handling method is very effective on the performance of both convergence and diversity.

关键词： improved epsilon constraint

Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

Energy-efficient resource allocation in noma heterogeneous networks

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arXiv 2018年

作者： Zhang, Haijun Fang, Fang Cheng, Julian Long, Keping Wang, Wei Leung, Victor C.M. Engineering and Technology Research Center for Convergence Networks University of Science and Technology Beijing Beijing100083 China School of Engineering University of British Columbia KelownaBCV1X 1V7 Canada College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Non-orthogonal multiple access (NOMA) has attracted much recent attention owing to its capability for improving the system spectral efficiency in wireless communications. Deploying NOMA in heterogeneous network can satisfy users' explosive data traffic requirements, and NOMA will likely play an important role in the fifth-generation (5G) mobile communication networks. However, NOMA brings new technical challenges on resource allocation due to the mutual cross-tier interference in heterogeneous networks. In this article, to study the tradeoff between data rate performance and energy consumption in NOMA, we examine the problem of energy-efficient user scheduling and power optimization in 5G NOMA heterogeneous networks. The energy-efficient user scheduling and power allocation schemes are introduced for the downlink 5G NOMA heterogeneous network for perfect and imperfect channel state information (CSI) respectively. Simulation results show that the resource allocation schemes can significantly increase the energy efficiency of 5G NOMA heterogeneous network for both cases of perfect CSI and imperfect CSI. Copyright © 2018, The Authors. All rights reserved.

关键词： Heterogeneous networks

Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

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Constrained Multi-scale Dense Connections for Accurate Biome...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Zhang Yanchun Zhang Shanfeng Zhu Xiaowei Xu Shanghai key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Engineering and Science Victoria University Melbourne Australia ISTBI and Shanghai Institute of Artificial Intelligence Algorithms Fudan University Shanghai China Nanjing University Institute of Artificial Intelligence Biomedicine Nanjing China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Guangdong Provincial People’s Hospital Guangdong Cardiovascular Institute Guangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and DeepLabV3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.

关键词： Decoding Image segmentation Image resolution Biomedical imaging Glands Fuses Cancer

Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking

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arXiv 2020年

作者： Liu, Li Chen, Da Shu, Minglei Li, Baosheng Shu, Huazhong Paques, Michel Cohen, Laurent D. Qilu University of Technology Shandong Academy of Sciences Shandong Artificial Intelligence Institute Jinan250014 China Shandong's Key Laboratory of Radiation Oncology Shandong Cancer Hospital Shandong Academy of Medical Sciences Jinan China Department of Radiation Oncology Shandong Cancer Hospital & Institute Shandong Academy of Medical Sciences Jinan China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China The Centre Hospitalier National dOphtalmologie des Quinze-Vingts Paris France University Paris Dauphine PSL Research University CNRS UMR 7534 CEREMADE Paris75016 France

Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Trees (mathematics)