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A cross-domain person re-identification algorithm based on distribution-consistency and multi-label collaborative learning

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Multimedia Tools and Applications 2024年第38期83卷 85939-85962页

作者： Zhang, Baohua Hao, Chen Lv, Xiaoqi Gu, Yu Wang, Yueming Liu, Xin Ren, Yan Li, Jianjun School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China School of Information Engineering Mongolia Industrial University Inner Mongolia Huhehaote010051 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia Baotou014010 China

To decrease domain shift in cross-domain person re-identification, existing methods generate pseudo labels for training models, however, the inherent distribution between source domain data and the hard quantization loss is ignored. Therefore, a cross-domain person re-identification method based on distribution consistency and multi-label collaborative learning is proposed. Firstly, a soft binary cross-entropy loss function is constructed to constrain the inter-sample relationship of cross-domain transformation, which can ensure the consistency of appearance features and sample distribution, and achieving feature cross-domain alignment. On this basis, in order to suppress the noise of hard pseudo labels, a multi-label collaborative learning network is constructed. The soft pseudo labels are generated by using the collaborative foreground features and global features to guide the network training, making the model adapt to the target domain. The experimental results show that the proposed method has better performance than that of recent representative methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Federated learning

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Faster and More Robust Low-Resource Nearest Neighbor Machine Translation 12th

Faster and More Robust Low-Resource Nearest Neighbor Machin...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Sun, Shuo Hou, Hongxu Yang, Zongheng Wang, Yisong College of Computer Science Inner Mongolia University National and Local Joint Engineering Research Center of Intelligent Information Hohhot China Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446955

Transformer-based neural machine translation (NMT) models have achieved performance close to human-level on some languages, but still suffer from poor interpretability and scalability of the models. Many advanced studies enhance the model’s translation ability by building external memory modules and utilizing retrieval operations, however, it suffers from poor robustness and low decoding efficiency while improving the model performance, especially for low-resource translation tasks. In this paper, we propose a confidence-based gating mechanism to optimize the decoding efficiency by building a sub-network to determine the confidence of the model’s own translation capability and then decide whether the current translation needs to be retrieved from the memory module. By reducing the number of retrievals to improve the model’s translation speed without degrading the translation quality as much as possible. In addition, we use a nonparametric dynamic Monte Carlo-based algorithm to fuse retrieval probabilities and model predictions to improve the generalization and robustness of the model. Extensive experiments on different datasets demonstrate the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neural machine translation

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A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

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Advances in Astronautics Science and technology 2024年第2期7卷 163-176页

作者： Chen, Xiaoyu Gao, Qi Peng, Shengren Song, Shihui Liu, Yufei Dai, Guangming Wang, Maocai Zhang, Changxing School of Computer China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Lumo Road Wuhan430074 China Qian Xuesen Laboratory China Academy of Space Technology Youyi Road Beijing100094 China

With the increasing number and capabilities of orbital satellites and their onboard sensors, satellites scheduling plays an important role in growing demands for Earth observation tasks. A conflict-priority-based variable neighborhood tabu search method is proposed in this paper. By analyzing the flexibility of resources, the conflict-priority of tasks, and the visible time window features, the indicators of the impact, the conflict, the flexibility, and the bad impact are first established. On this basis, a first-come first-served greedy algorithm and a minimum bad impact greedy algorithm are designed for the generation of the initial solution. some rule-based heuristic strategies are also adopted for optimizing. Additionally, the algorithm incorporates three types of neighborhood structures including insertion, swapping and rearrangement, and deletion, using a tabu list to avoid local optima. Experimental results indicate the validity and efficiency of the proposed method. All solutions are within 10% of the optimal solution, with some even within 5%.The proposed method is effective for obtaining optimum solutions or solutions with a very good quality in various multi-satellite scheduling scenarios of realistic planning. © Chinese Society of Astronautics 2024.

关键词： Tabu search

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Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2099-2110页

作者： Meng Wang Huaicheng Yan Jianbin Qiu Wenqiang Ji Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationSchool of Information Science and EngineeringEast China University of Science and TechnologyShanghai 200237China Research Institute of Intelligent Control and Systems School of AstronauticsHarbin Institute of TechnologyHarbin 150080China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China IEEE

This article studies the fault detection filtering design problem for Roesser type two-dimensional(2-D)nonlinear systems described by uncertain 2-D Takagi-Sugeno(T-S)fuzzy ***,fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited,based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to *** has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional nonfrequency based filtering design ***,with the proposed evaluation function and its threshold,a novel mixed finite frequency H_(∞)/H_(-)fault detection algorithm is developed,based on which the fault can be immediately detected once the evaluation function exceeds the ***,it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.

关键词： Fault diagnosis finite frequency specifications mixed H_(∞)/H_(-)performance two-dimensional nonlinear systems

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Multiband Kick Drum Separation with Genre-Guided Optimization 10th

Multiband Kick Drum Separation with Genre-Guided Optimizati...

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10th Conference on Sound and Music technology, CSMT 2023

作者： Li, Si Jiang, Yuanfeng Wang, Zhaowen Qian, Jiale Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Department of Music AI and Information Technology Central Conservatory of Music Beijing100091 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

Kick drum plays an important role in electronic music. When hearing an excellent kick drum sound from a reference music track, music production beginners always feel confused about how it will sound if we solo the kick track and why it sounds perfect in a specific song. Reproducing the kick of a music track is not easy even for professional producers and arrangers. To investigate the ability of artificial intelligence in separating kick-drum sounds, in this paper we propose an end-to-end system which is capable of separating multi-genre high-quality electronic kick drum clips from mixed song tracks. Unlike most of the previous music source separation tasks, we only focus on the electronic kick drum rather than the whole percussion track. First, we design a multiband data representation approach for non-pitched percussions such as kick drums, then adopt a multi-discriminator module trained on a multi-genre kick drum corpus to further optimize the separated kick sound. To the authors’ knowledge, this study marks the first attempt at kick drum separation with stylistic optimization. Objective and subjective evaluations demonstrate that joint multiband representation and genre-guided optimisation can form a high-performance framework, showing a promising solution to the kick drum separation task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Source separation

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Tracking Control in Presence of Obstacles and Uncertainties for Bioinspired Spherical Underwater Robots

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Journal of Bionic Engineering 2023年第1期20卷 323-337页

作者： Chunying Li Shuxiang Guo Jian Guo Graduate School of Engineering Kagawa UniversityTakamatsuKagawa761-0396Japan Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Information TechnologySchool of Life Science and TechnologyBeijing Institute of TechnologyBeijing100081China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of TechnologyBinshui Xidao Extension 391Tianjin300384China

During marine missions,AUVs are susceptible to external disturbances,such as obstacles,ocean currents,etc.,which can easily cause mission failure or *** this paper,considering the strong nonlinearities,external disturbances and obstacles,the kinematic and dynamic model of bioinspired Spherical Underwater Robot(SUR)was ***,the waypoints-based trajectory tracking with obstacles and uncertainties was proposed for SUR to guarantee its safety and ***,the Lyapunov theory was adopted to verify the stability and the Slide Mode Control(SMC)method is used to verify the robustness of the control *** addition,a series of simulations were conducted to evaluate the effectiveness of proposed control *** tests,including path-following,static and moving obstacle avoidance were performed which verified the feasibility,robustness and effectiveness of the designed control ***,a series of experiments in real environment were performed to verify the performance of the control *** simulation and experimental results of the study supplied clues to the improvement of the path following capability and multi-obstacle avoidance of AUVs.

关键词： Path following Obstacle avoidance Bioinspired.Spherical underwater robot(SUR) Lyapunov theory

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

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Generating Molecules Conditional on 3D Protein Pockets with HGAF

Generating Molecules Conditional on 3D Protein Pockets with ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Qinglian Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350337488

Most current representations of protein pockets are the atom-pair graph, which ignore the global structural information of amino acids. Therefore, we propose a new molecular generation model, which uses the hypergraph to represent protein pocket structure, and combines the structural features obtained by atom-pair graph representation. These two levels of graphs are more capable of representing the complex structural information of protein pockets. Then, the graphs of the two levels of the protein pockets are input into the improved network model, which is named Hypergraph Graph Attention Fusion (HGAF), to obtain the embedding representation of the protein pockets, which is used as a condition to constrain the molecule generation. The molecules sampled by HGAF are subjected to quality assessment and docking targeting validation. Experimental results show that the molecules generated by proposed method can achieve better results in both of these assessment approaches. © 2023 IEEE.

关键词： 3D Protein Pockets Drug Design Graph Attention Networks

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A Microstrip Magnetic Dipole Antenna with Reconfigurable Beamwidth 6

A Microstrip Magnetic Dipole Antenna with Reconfigurable Bea...

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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Liang, Renhang Liang, Zhixi Li, Yuanxin Jiang, Hongyan Long, Yunliang School of Electronics and Information Technology Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China

ISBN: (纸本)9798350399059

This paper presents a novel microstrip magnetic dipole (MD) antenna with reconfigurable beamwidth. The proposed design is composed of a microstrip magnetic antenna and two metal planes. The angle of the metal planes is adjustable, and the metal planes are used as a reconfigurable corner reflector. As the angle varies, the reflection coefficients in the operating band (5.67-5.91GHz) are retained below -10dB, and the E-plane half-power beamwidth (HPBW) varies from 37° to 142°. The maximum gain of 12.1 dBi is obtained when the beamwidth is 37°, and the minimum gain of 8.9 dBi is obtained when the beamwidth is 142°. The proposed design is a good candidate for base station applications to enhance traffic capacity. © 2023 IEEE.

关键词： Microstrip antennas

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