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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Twitter United States

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match query and support videos. However, they still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. In addition, we explicitly exploit the temporal coherence in videos to regularize the matching process. In this way, HyRSM++ facilitates informative correlation exchanged among videos and enables flexible predictions under the data-limited scenario. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmark

关键词： Benchmarking

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A novel method of constructing high-dimensional digital chaotic systems on finite-state automata

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Chinese Physics B 2020年第9期29卷 233-243页

作者： Jun Zheng Han-Ping Hu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.

关键词： high-dimensional digital chaotic system dynamical degradation anti-control pseudorandom number generator

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Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

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Frontiers of Computer Science 2020年第5期14卷 99-112页

作者： Lei Chen Kai Shao Xianzhong Long Lingsheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210023China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and AstronauticsNanjing211106China

Survival analysis aims to predict the occurrence time of a particular event of interest,which is crucial for the prognosis analysis of ***,due to the limited study period and potential losing tracks,the observed data inevitably involve some censored instances,and thus brings a unique challenge that distinguishes from the general regression *** addition,survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size *** address these challenges,we propose a novel multi-task regression learning model,i.e.,prior information guided transductive matrix completion(PigTMC)model,to predict the survival status of the new ***,we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples,and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size *** addition,we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival ***,we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC ***,the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

关键词： survival analysis matrix completion multi-task regression transductive learning multi-task feature selection

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KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings 29

KGE-CL: Contrastive Learning of Tensor Decomposition Based K...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Luo, Zhiping Xu, Wentao Liu, Weiqing Bian, Jiang Yin, Jian Liu, Tie-Yan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding (KGE). However, most previous KGE methods ignore the semantic similarity between the related entities and entity-relation couples in different triples since they separately optimize each triple with the scoring function. To address this problem, we propose a simple yet efficient contrastive learning framework for tensor decomposition based (TDB) KGE, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the performance of KGE. We evaluate our proposed method on three standard KGE datasets: WN18RR, FB15k-237 and YAGO3-10. Our method can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, 37.8% MRR, 28.6% Hits@1 on FB15k-237 dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset. Source codes and data of this paper can be found at https://***/Wentao-Xu/KGE-CL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Knowledge graph

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Modeling of Solid Oxide Electrolysis Cell with Oxygen Pressure at the Oxygen electrode/Electrolyte Interface

Modeling of Solid Oxide Electrolysis Cell with Oxygen Pressu...

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第34届中国控制与决策会议

作者： Zhiping Xia Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science & Technology

As one of the key degradation mechanisms of Solid oxide electrolysis cell(SOEC),oxygen electrode delamination directly affects the durability of *** order to improve the service life of SOEC stack,optimizing the system regulation range is an effective *** this study,according to the delamination mechanism of SOEC oxygen electrode,based on the existing thermal and electrical characteristics of the SOEC stack,a new gas characteristic of oxygen electrode/electrolyte interface oxygen pressure is added,and a cross-flow SOEC 2-D stack model with the oxygen electrode/electrolyte interface oxygen pressure was ***,the influence of the oxygen electrode/electrolyte interface oxygen pressure characteristics on the safe operating range of the stack is *** simulation results show that the safe operating range of the system was optimized by adding it.

关键词： Solid oxide electrolysis cell Durability Delamination mechanism 2-D model Advantage analysis

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USV Path Planning Based on ACO-SA Algorithm

USV Path Planning Based on ACO-SA Algorithm

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Chinese Control Conference (CCC)

作者： Chuanshang Luo Yifeng Tang Zhihui Huang Yifan Xu Bin Liu Hai-Tao Zhang the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization algorithm. First, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running; change the heuristic function and state transition probability taking three factors into consideration; change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same algorithm. Finally, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm.

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Multi-Sequence MRI Thoracic Spine Compression Fracture Classification Based on Local-Global Feature Selection and Cross-Attention Fusion

Multi-Sequence MRI Thoracic Spine Compression Fracture Class...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Shuhua Jin Jinjin Hai Shijie Wei Jian Chen Kai Qiao Weicong Zhang Hai Lv Bin Yan Henan Key Laboratory of Imaging and Intelligent Processing School of Cyber Science and Engineering Zhengzhou University Information Engineering University Zhengzhou Henan China Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou Henan China Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou University Zhengzhou Henan China Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application Jiangmen Guangdong China Department of Spinal Surgery The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai Guangdong China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Thoracic vertebral compression fracture (VCF) is a common fracture type in clinical practice, which seriously affects the quality of life of patients. The benign and malignant classifications of VCF are of significant diagnostic importance for the selection of clinical treatment options. Magnetic resonance imaging (MRI), as the main diagnostic tool for VCF, has the characteristics of multi-sequence imaging, and the structural and tissue information provided by different sequences complement each other. This paper proposes a multi-sequence MRI feature fusion network CAMSF based on vision transformer (ViT), which solves the problems of limited local feature extraction of ViT and insufficient interaction between multi-sequence image features. This method combines a multi-head self-attention mechanism and a deep convolution module to effectively capture global and local features, and uses a graph pooling-based feature selection method to optimize redundant features. In addition, a fusion method based on the cross-attention mechanism is used to deeply interact with different MRI sequence features to obtain complementary information across sequence images. Experimental results on the thoracic VCF dataset show that the classification accuracy of CAMSF reaches 84.88% and the AUC reaches 83.39%, which is significantly better than the existing ViT-based multimodal image feature fusion method. The ablation experiment further verifies the effectiveness of each module, indicating that CAMSF can effectively fuse multi-sequence MRI features and provide an effective solution for the accurate classification of VCF.

关键词： Computer vision Accuracy image coding Control engineering Convolution Magnetic resonance imaging Sensitivity and specificity Feature extraction Transformers Cancer

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Whale optimisation algorithm with role labour division for multimodal optimisation

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International Journal of Innovative Computing and Applications 2024年第1期15卷 26-37页

作者： Wu, Bowen Xiao, Renbin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education of China Huazhong University of Science and Technology Hubei Wuhan430074 China

The multimodal optimisation problem challenges the balance between diversity and convergence, which poses a great degree of challenge to traditional population-based intelligence optimisers. The whale optimisation algorithm has the limitation of easily falling into local optimum when facing multimodal optimisation. To address this problem, this paper proposes a whale optimisation algorithm with role labour division (WOA-RDL). In WOA-RDL, a multi-stage role division of labour mechanism is designed to divide the optimisation process into two stages of group hunting. In the first stage, the task allocation in the hunting process is considered as two types of search tasks: exploration and exploitation. The external environmental stimulus of the task and the internal response threshold of the individual were designed to allow individuals to switch between exploration and exploitation tasks flexibly. In the second stage, the distribution mechanism of different roles was designed to simulate the 'strong to weak' distribution rule for the distribution of results after the hunt. The elite roles are selected to further strengthen their capabilities or remain robust, while the inferior roles are predicted and eliminated to improve the overall optimisation of the population and reduce the risk of falling into a local optimum. The results of the optimisation experiments using multimodal, unimodal, CEC2017 combined functions and CEC2009 dynamic functions to validate the effectiveness of the algorithm. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Optimization algorithms

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Dynamic Graph Dismantling with Reinforcement Supervised Learning

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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Chinese Control Conference (CCC)

作者： Yumei Wang Chuancong Tang Hai-Tao Zhang the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

There are always some “key” nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the “key” nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, $\epsilon$ -greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization.

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WDNet: A Water Depth Perception Network with Global Information

WDNet: A Water Depth Perception Network with Global Informat...

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Chinese Control Conference (CCC)

作者： Zhihui Huang Yifeng Tang Chuancong Tang Yifan Xu ChuanShang Luo Bin Liu Hai-Tao Zhang the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems Huazhong University of Science and Technology Wuhan China

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures (sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network (WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image, and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model.

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