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arXiv 2021年

作者： Qing, Zhiwu Su, Haisheng Gan, Weihao Wang, Dongliang Wu, Wei Wang, Xiang Qiao, Yu Yan, Junjie Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China SenseTime Research Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Laboratory Shanghai China

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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A Research of Heart Rate Prediction Model Based on Evolutionary Neural Network

A Research of Heart Rate Prediction Model Based on Evolution...

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International Conference on intelligent Computation and Bio-Medical Instrumentation (ICBMI)

作者： Feng Xiao Ming Yuchi Mingyue Ding Jun Jo Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Life College of Science and Technology Huazhong University of Science and Technology Wuhan China School of Information and Communication Technology Griffith University QLD Australia

Heart rate (HR) signal analysis is widely used in the medicine and medical research area. Physical activities (PA) are commonly recognized to greatly affect the changes of heart rate. A method of Evolutionary Neural Network - Neuro-evolution of Augmenting Topologies (NEAT) is used to build a PA-based HR predictor model. Through special coding, crossover and mutation operator, NEAT can implement network topology and connectivity weights evolution simultaneously. The common problem in evolutionary neural network, like competing conventions, how to protect the new innovation are effectively solved. The experimental results demonstrated the application potential of the approach.

关键词： Heart rate Biological neural networks Training Monitoring Neurons Encoding Biomedical monitoring

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SPATIALLY ATTENTIVE CORRELATION FILTERS FOR VISUAL TRACKING

SPATIALLY ATTENTIVE CORRELATION FILTERS FOR VISUAL TRACKING

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IEEE International Conference on image processing

作者： Huai Qin Zhixiong Pi Changqian Yu Changxin Gao Jin-Gang Yu Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology Wuhan Hubei CN South China University of Technology Guangzhou China

Although correlation filter based trackers have recently demonstrated excellent performance, they still suffer from the boundary effects. The cosine window is introduced to alleviate the boundary affects, which however may result in poor performance in case of occlusion or fast motion. To address this problem, we propose a simple yet effective framework, which builds a spatially attentive model with multiple features to guide the detection of the correlation filter based trackers. The proposed method not only can breakthrough the spatial extent of cosine window, but also can provides prior information about the target object. Moreover, to model a robust object prior, we propose a generic strategy for adaptive fusion and update of multiple features. Extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our methods compared to the state-of-the-art trackers.

关键词： Target tracking Correlation Adaptation models Color Training Mathematical model

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Analysis of a memristor-based switching network

Analysis of a memristor-based switching network

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International Conference on Information Science and Technology (ICIST)

作者： Ailong Wu Yi Shen Zhigang Zeng Jine Zhang Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Basic Science East China Jiao Tong University Nanchang China

ISBN: (纸本)9781424494408

In this paper, we formulate and investigate a memristor-based switching network which is directly extended from Itoh and Chua's chaotic circuit. Conditions are derived which ensure the existence of an equilibrium point and the uniformly stable for state trajectories of the memristor-based switching network. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. It is believed that the criteria in this paper is also valuable in the design of memristor-based switching network which can be used to solve efficiently classes of optimization problems arising in practical engineering applications.

关键词： Chaos Switches

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Data-driven Sparse Bayesian Learning Discovery of Hysteresis Effects in Electrospinning Processes

Data-driven Sparse Bayesian Learning Discovery of Hysteresis...

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第34届中国控制与决策会议

作者： Hongwei Sun Yasen Wang Yaozhong Zheng Xiang Huang Hai-Tao Zhang School of Artificial Intelligence and Automation and Key Laboratory of Image Processing and Intelligent ControlSchool of AutomationHuazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology

Electrospinning is an efficient and feasible method to fabricate hairline fibers from polymers or ***,the hysteresis nonlinearity among pump flow and jet diameter during electrospinning process strongly hinders the improvement of its *** alleviate this effect,it is an urgent yet challenging mission to discover hysteresis effects in near-field Electrospinning *** this work,a nonlinear autoregressive exogenous model is developed for described electrospinning ***,a pure data-driven sparse B ayesian learning(SBL) method is applied to distill the pump flow-jet diameter hysteresis ***,extensive experiments are conducted to verify the discovered effects by the SBL identification method.

关键词： Electrospinning hysteresis nonlinear autoregressive exogenous(NARX) sparse Bayesian learning(SBL)

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Hu Jiaming Cai Wangjie Zheng Yang Yang Hong-Bin Shen Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.

关键词： Three-dimensional displays Feature extraction image reconstruction Task analysis Solid modeling Electron microscopy Convolutional neural networks

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Solving NP-complete problems by spiking neural P systems with budding rules

Solving NP-complete problems by spiking neural P systems wit...

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10th International Workshop on Membrane Computing, WMC 2009

作者： Ishdorj, Tseren-Onolt Leporati, Alberto Pan, Linqiang Wang, Jun Computational Biomodelling Laboratory Åbo Akademi University Department of Information Technologies 20520 Turku Finland Università degli Studi di Milano - Bicocca Dipartimento di Informatica Sistemistica e Comunicazione Viale Sarca 336/14 20126 Milano Italy Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Research Group on Natural Computing Department of CS and AI University of Sevilla Avda Reina Mercedes s/n 41012 Sevilla Spain

ISBN: (纸本)3642114660

Inspired by the growth of dendritic trees in biological neurons, we introduce spiking neural P systems with budding rules. By applying these rules in a maximally parallel way, a spiking neural P system can exponentially increase the size of its synapse graph in a polynomial number of computation steps. Such a possibility can be exploited to efficiently solve computationally difficult problems in deterministic polynomial time, as it is shown in this paper for the NP-complete decision problem sat. © 2010 Springer-Verlag.

关键词： Computational complexity

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Fast Marine Route Planning for UAV Using Improved Sparse A* Algorithm

Fast Marine Route Planning for UAV Using Improved Sparse A* ...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Xin Yang Ming-yue Ding Cheng-ping Zhou Institute for Pattern Recognition and Artificial Intelligence (IPRAI) Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China School of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Institute for Pattern Recognition and Artificial Intelligence (IPRAI) State Key Laboratory for Multispectral Information Processing Technologies Huazhong University of Science and Technology Wuhan Hubei China

This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.

关键词： Unmanned aerial vehicles Planning Synthetic aperture sonar Cost function Algorithm design and analysis Heuristic algorithms Leg

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Deterministic-like data-driven discovery of stochastic differential equations via the Feynman–Kac formalism

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The European Physical Journal Special Topics 2024年 1-21页

作者： Ma, Chaoxiang Huang, Cheng Cheng, Cheng Li, Xiuting The College of Informatics Huazhong Agriculture University and the China–Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan People’s Republic of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Ministry of Education Key Lab of Intelligent Control and Image Processing Wuhan China

This paper develops a data-driven deterministic identification architecture for discovering stochastic differential equations (SDEs) directly from data. The architecture first generates deterministic data for stochastic processes using the Feynman–Kac formula, and gives a parabolic partial differential equation (PDE) associated with the SDE. Then, a sparse regression model is proposed to discover drift and diffusion terms in SDEs using PDE data-driven techniques, where a large candidate library of potential terms only for the drift and diffusion coefficients in SDEs need be constructed. To simultaneously infer the drift and diffusion terms, we proposed a sequential thresholded reweighted least-squares algorithm to solve the constructed sparse regression model. The main advantage of the proposed method is that on the one hand, theoretical and numerical identification results of PDEs can be used for SDEs, on the score, our SDE identification problem is translated into the parameter estimation problem of PDEs, on the other hand, the proposed algorithm is easily executed and can enhance the sparsity and accuracy. Through several classical SDEs and ordinary differential equations, the effectiveness of the proposed data-driven method is demonstrated, and several comparison experiments with state-of-the-art approaches is provided to illustrate the superiority of the developed algorithm.

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