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arXiv 2024年

作者： Wu, Hongjie He, Linchao Zhang, Mingqin Chen, Dongdong Luo, Kunming Luo, Mengting Zhou, Ji-Zhe Chen, Hu Lv, Jiancheng College of Computer Science Sichuan University Chengdu China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China Heriot-Watt University Edinburgh United Kingdom Hong Kong University of Science and Technology Hong Kong Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep. Additionally, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate that our proposed method significantly enhances image restoration performance while consuming negligible additional computational resources. Copyright © 2024, The Authors. All rights reserved.

关键词： White noise

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

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第35届中国控制与决策会议

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Engineering Laboratory for Intelligent Industrial Vision CAS State Grid Intelligent Technology Co.Ltd. State Grid Shandong Electric Power Company Jinan Power Supply Company

In order to meet the operational needs in some narrow environments,the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of *** present,the research of hyper-redundant manipulator mainly focuses on the straight push *** the limitation of working space,this paper designed a hyper-redundant manipulator delivered by a curled *** to the characteristics of turntable delivery,a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are ***,with the algorithm applied to the physical robot we made,we successfully realized the tipfollowing motion control of the hyper-redundant manipulator.

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Simulations and Experiments of Time-Varying Takagi Factorization via Zhang Neural Network

Simulations and Experiments of Time-Varying Takagi Factoriza...

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IEEE Advanced Information technology, Electronic and Automation control Conference (IAEAC)

作者： Zanyu Tang Tianyu Zeng Zhenyu Li Jianrong Chen Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Modern Control Technology Guangzhou China College of Information Science and Engineering Jishou University Jishou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Public Health Administration Youjiang Medical University for Nationalities Baise China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

The problem of Takagi factorization (TF) for complex time-varying symmetric matrix is proposed in this paper. To solve this difficult and challenging problem, a continuous-time TF (CTTF) model of derivative equation system form is proposed via Zhang neural network (ZNN) method. For the sake of digital hardware implementation, we also build up the corresponding discrete-time (DT) ZNN solution models with different precisions of the derivative equation system discretization. Specifically, an eight-instant DTTF (EIDTTF) model are proposed. We also comparatively use two other multi-instant (e.g., four-instant and six-instant, i.e., FIDTTF and SIDTTF, respectively) models by discretizing the CTTF model. The effective solution step-size intervals of the proposed DTTF models are further discussed lastly. The results of simulations and experiments substantiate the correctness, effectiveness and superiority of the proposed CTTF and DTTF models.

关键词： Symmetric matrices Conferences Neural networks Hardware Numerical models Mathematical model Information technology

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Query-guided Prototype Evolution Network for Few-Shot Segmentation

arXiv

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arXiv 2024年

作者： Cong, Runmin Xiong, Hang Chen, Jinpeng Zhang, Wei Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries. The evolution of the foreground prototype is accomplished through a support-query-support iterative process involving two new modules: Pseudo-prototype Generation (PPG) and Dual Prototype Evolution (DPE). The PPG module employs support features to create an initial prototype for the preliminary segmentation of the query image, resulting in a pseudo-prototype reflecting the unique needs of the current query. Subsequently, the DPE module performs reverse segmentation on support images using this pseudo-prototype, leading to the generation of evolved prototypes, which can be considered as custom solutions. As for the background prototype, the evolution begins with a global background prototype that represents the generalized features of all training images. We also design a Global Background Cleansing (GBC) module to eliminate potential adverse components mirroring the characteristics of the current foreground class. Experimental results on the PASCAL-5i and COCO-20i datasets attest to the substantial enhancements achieved by QPENet over prevailing state-of-the-art techniques, underscoring the validity of our ideas. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Self-Organized Criticality in C. Elegans Neural Network

Self-Organized Criticality in C. Elegans Neural Network

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Chinese control Conference (CCC)

作者： Xiao Gu Jian Liu Ye Yuan Peng Zhao Kuankuan Xin Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China Queensland Brain Institute The University of Queensland Brisbane QLD Australia

The self-organized criticality is a fundamental property for organisms to perform normal functional activities. Extensive large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments.

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Copy and paste GAN: Face hallucination from shaded thumbnails

arXiv

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arXiv 2020年

作者： Zhang, Yang Tsang, Ivor W. Luo, Yawei Hu, Changhui Lu, Xiaobo Yu, Xin School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China School of Automation Nanjing University of Posts and Telecommunications China Australian Centre for Robotic Vision Australian National University Australia

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and nonuniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details;while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternately in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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No 8-Instant ZTD (Zhang Time Discretization) Formula with Quintic Precision or Higher as Proved

No 8-Instant ZTD (Zhang Time Discretization) Formula with Qu...

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Chinese Automation Congress (CAC)

作者： Yunong Zhang Kangze Zheng Jinjin Guo School of Computer Science and Engineering Sun Yat-sen University Guangzhou P. R. China Guangdong Key Laboratory of Modern Control Technology Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

Since 2014, Zhang time discretization (ZTD, also termed Zhang et al. discretization) formulas have been put forward as a new method for time discretization by Zhang et al. Originally, ZTD formulas were used to obtain discrete Zhang neural network (ZNN) model from continuous ZNN one. ZTD formulas were also used later to discretize other continuous-time systems. So far, ZTD formulas with various number of instants have been proposed and applied, and the highest precisions of the 2-instant ZTD formula to 7-instant ZTD formulas have been discovered. Instinctively, we want to find out the highest precision of 8-instant ZTD formulas. During our investigation, we ascertain that any convergent 8-instant ZTD formula cannot possess quintic precision or higher. Combined with previous work, we conclude that any 8-instant ZTD formula converges with a truncation error in proportion to the 4-th power of the sampling interval or greater. The conclusion is proved in our paper.

关键词： Automation Stability criteria Neural networks Finite wordlength effects Thermal stability

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Learning Hierarchical Color Guidance for Depth Map Super-Resolution

arXiv

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arXiv 2024年

作者： Cong, Runmin Sheng, Ronghui Wu, Hao Guo, Yulan Wei, Yunchao Zuo, Wangmeng Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong Jinan250061 China Artificial Intelligence Institute Beijing Normal University Beijing100875 China Changsha410073 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Lingnan University Hong Kong

Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively. The code and results can be found from the link of https://***/HCGNet_TIM2024. © 2024, CC BY-NC-SA.

关键词： Restoration

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A Hybrid CNN-Transformer for Focal Liver Lesion Classification

A Hybrid CNN-Transformer for Focal Liver Lesion Classificati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ling Zhao Shuaiqi Liu Bing Li Wenjia Cai Ping Liang Jie Yu Jie Zhao School of Quality and Technical Supervision Hebei University Baoding China College of Electronic and Information Engineering Hebei University Machine Vision Technology Innovation Center of Hebei Province Baoding China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Interventional Ultrasound The Fifth Medical Center Chinese PLA General Hospital Beijing China

The early diagnosis of focal liver lesions (FLLs) plays a key role in the successful treatment of liver cancer. To effectively diagnose focal liver lesions, we used contrast-enhanced ultrasound (CEUS) to diagnose FLLs. A hybrid CNN and Transformer network is used to extract local and global spatio-temporal features of CEUS. Firstly, the R(2+1)D with pre-trained weights is used to extract local multi-scale spatio-temporal features, and then the feature maps with various size are input into the G- 1 Transformer for global information learning and interaction. To reduce the parameters of the traditional Transformer, a new efficient Transformer named G-Transformer is proposed to achieve better performance with lower parameters. The proposed model was evaluated on a multi-center-multi-disease dataset, and the results showed that the proposed model can achieve an AUC of 0.8237 and better generalization performance.

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