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A comprehensive survey of visible infrared person re-identification from an application perspective

Multimedia Tools and Applications 2024年第42期83卷 90243-90270页

作者： Chang, Hua Xu, Xin Liu, Wei Lu, Lingyi Li, Weigang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V→IR), retrieval of visible images using infrared images (IR→V), and joint retrieval of infrared and visible images (V→IR and IR→V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Open Grounded Planning: Challenges and Benchmark Construction 62

Open Grounded Planning: Challenges and Benchmark Constructio...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Guo, Shiguang Deng, Ziliang Lin, Hongyu Lu, Yaojie Han, Xianpei Sun, Le Chinese Information Processing Laboratory China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of System Software Chinese Academy of Sciences China

ISBN: (纸本)9798891760943

The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generation capabilities of LLMs to produce free-style plans or employs reinforcement learning approaches to learn decision-making for a limited set of actions within restricted environments. However, both approaches exhibit significant discrepancies between the open and executable requirements in real-world planning. In this paper, we propose a new planning task-open grounded planning. The primary objective of open grounded planning is to ask the model to generate an executable plan based on a variable action set, thereby ensuring the executability of the produced plan. To this end, we establish a benchmark for open grounded planning spanning a wide range of domains. Then we test current state-of-the-art LLMs along with five planning approaches, revealing that existing LLMs and methods still struggle to address the challenges posed by grounded planning in open domains. The outcomes of this paper define and establish a foundational dataset for open grounded planning, and shed light on the potential challenges and future directions of LLM-based planning. Our code and datasets are at https://***/Shiguang-Guo/Open-Grounded-Planning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Modeling of All-Vanadium Redox Battery Based on Dual Stream Network with Attention Mechanism 10

Modeling of All-Vanadium Redox Battery Based on Dual Stream ...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Zhu, Jiajie Yang, Bo Cai, Mingxuan Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Accurate voltage prediction for all-vanadium redox batteries (VRBs) affects the efficiency and safety of battery. In this paper, we propose a self-attention-based dual-stream neural network (SA-DSNN) model for voltage prediction under various working conditions. This approach independently processes temporal and condition data streams to reduce the noise interference. Using neural networks tailored to each of stream can more effectively extract intrinsic features. In particular, self-attention mechanisms are employed to enhance critical information. The features from both streams are fully integrated in output layer to generate final predictions. Experimental results indicate that the proposed method enhances the generalization and accuracy of VRBs model across different time scale. © 2024 IEEE.

关键词： all-vanadium redox battery dual stream network self-attention mechanism voltage prediction

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Stability Analysis of Boolean Networks: Exploring the Influence of Multi-bit Function Perturbations 63

Stability Analysis of Boolean Networks: Exploring the Influe...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Aixin Wang, Lin Chen, Guanrong Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China City University of Hong Kong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798350316339

This study investigates the impact of multi-bit function perturbations (MFPs) on the steady-state distribution of Boolean networks (BNs), with a focus on robust stability. First, the algebraic formulation of BNs under MFPs is introduced. Then, a novel stability criterion is proposed, demonstrating that monotonically decreasing perturbations can effectively ensure the stability of BNs against MFPs. A significant advantage of this approach is that it enables robust stability assessment without remodeling the perturbed BNs. Applying this methodology to a simplified Boolean model of the lactose operon in Escherichia coli reveals that MFPs can serve as strategic tools to promote stability within BNs, rather than being merely disruptive. © 2024 IEEE.

关键词： Stability criteria

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Distributed dynamic event-triggered adaptive neural resilient control for switched multiagent systems against attacks

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Nonlinear Dynamics 2025年 1-18页

作者： Li, Xuejiao Long, Lijun College of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

This paper investigates the distributed dynamic event-triggered adaptive neural resilient control problem for switched nonlinear multi-agent systems (MASs). A distributed switched observer and multiple Lyapunov functions (MLFs) are constructed to deal with directed switching topologies that are disconnected or paralyzed under denial-of-service (DoS) attacks. Further, by developing a switching MLFs method, DoS attacks on sampled switching signal and switched sampled errors that may compromise system stability are handled. Also, to address asynchronous switching between candidate subsystems and controllers, a new switching dynamic event-triggering mechanism and distributed event-triggered neural resilient controllers of subsystems are designed. Then, the secure leader-following consensus can be achieved and a positive constant lower bound on inter-event times is guaranteed to avoid the Zeno phenomenon. Finally, a practical example of a switched scenario from offshore ship crane-vessel MASs is proposed to demonstrate the effectiveness of the developed design strategies.

关键词： Distributed event-triggered control Adaptive neural resilient control Switched multi-agent systems Switching multiple Lyapunov functions

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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

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Chinese Journal of Electronics 2023年第5期32卷 972-981页

作者： CHEN Xiaohan HU Xiaoxi WEN Tao CAO Yuan The School of Electronic and Information Engineering Beijing Jiaotong University The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong University

In the railway transportation industry,fault diagnosis of railway point machines(RPMs) is *** operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signalbased fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently *** overcome these shortcomings, we propose a doublescale wide first-layer kernel convolutional neural network(DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for lowsampling-rate signals. Meanwhile, the proposed doublescale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison,were conducted to demonstrate the robustness and accuracy of the proposed algorithm.

关键词： Vibrations Fault diagnosis Transportation industry Feature extraction Rail transportation Robustness Convolutional neural networks

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A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults 13

A Cloud-Edge-Based Multi-Label Learning and Fast Clustering ...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Liu, Qi Yang, Bo Liu, Yuxiang Ma, Kai Guan, Xinping Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： K-means clustering

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhang, Jinghao Chen, Zhe Li, Ning Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798350327182

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling. © 2023 IEEE.

关键词： Genetic algorithms

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space 39

Learning with Open-world Noisy Data via Class-independent Ma...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)157735897X

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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