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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Hou, Zhiming Cao, Yuanzhouhan Beijing Jiaotong University Ministry of Education School of Computer and Information Technology Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing100044 China

ISBN: (纸本)9798350371024

Robust lane detection in real-time is one of the foundations for advanced autonomous driving, which can provide substantial amounts of useful information for Autonomous Driving Systems, vehicle self-control, localization, and map construction. Since the in/extrinsic parameters of different vehicles are various which has a significant impact on the results of 3D lanes, we introduce the Virtual Camera that unifies the in/extrinsic parameters of cameras mounted on different vehicles to guarantee the consistency of the spatial relationship among cameras. It can effectively promote the learning procedure due to the unified visual space. Different from using fixed intrinsic parameters and extrinsic parameters to transform front-view images to virtual images, we train a network, termed FVHNet, that estimates the parameters of an ideal perspective transformation, conditioned on the input image. In order to train FVHNet for outputting the transformation matrix that is optimal for perspective transformation, we construct a loss function referred FVHLoss. In summary, we propose a method which can unify the in/extrinsic parameters of different vehicles with learnable homography matrix. We verify our method on the Apollo 3D Lane Synthetic dataset and achieve competitive results. © 2023 IEEE.

关键词： Autonomous vehicles

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A Method for Reconstructing Three-Dimensional Structure of Nasal Cavity Based on a Short-Source Acoustic Tube 4th

A Method for Reconstructing Three-Dimensional Structure of N...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Lian, Xiaoqin Wang, Yuqiao Gao, Chao Li, Jin Ma, Guochun Wang, Zitong Bao, Kexin School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789819778867

The reconstruction of the three-dimensional structure of the nasal cavity is significant for the diagnosis and treatment of nasal and nasopharynx diseases. This study proposes a method for reconstructing the three-dimensional structure of the nasal cavity based on the short-source acoustic tube, which addresses the inconvenient portability and high cost of the long-source acoustic tube. Firstly, we separate incident and reflected waves based on the propagation characteristics of pulsed acoustic waves in a short-source acoustic tube. Secondly, to reduce the error in calculating the input impulse response sequence, the methods of correcting the direct current (DC) offset component and suppressing the high-frequency noise are used to pre-process the nasal acoustic signals. Finally, we adopt the layered reconstruction method of the three-dimensional structure of the nasal cavity to reconstruct the depth cross-sectional area two-dimensional curve and take a three-dimensional visualization. Through comparative analysis, this method can meet practical application requirements. © The Author(s) 2025.

关键词： Impulse response

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EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization 41

EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Mu...

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41st International Conference on Machine Learning, ICML 2024

作者： Gu, Zhibin Li, Zhendong Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China

This paper presents an efficient and scalable incomplete multi-view clustering method, referred to as Enhanced Dictionary-Induced tenSorized incomplete multi-view clustering with Gaussian errOr raNk minimization (EDISON). Specifically, EDISON employs an enhanced dictionary representation strategy as the foundation for inferring missing data and constructing anchor graphs, ensuring robustness to less-than-ideal data and maintaining high computational efficiency. Additionally, we introduce Gaussian error rank as a concise approximation of the true tensor rank, facilitating a comprehensive exploration of the diverse information encapsulated by various singular values in tensor data. Furthermore, we integrate a hyper-anchor graph Laplacian manifold regularization into the tensor representation, allowing for the simultaneous utilization of inter-view high-order correlations and intra-view local correlations. Extensive experiments demonstrate the superiority of the EDISON model in both effectiveness and efficiency compared to SOTA methods. Copyright 2024 by the author(s)

关键词： Tensors

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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on intelligence technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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Genomics, Proteomics & Bioinformatics 2024年第4期22卷 181-190页

作者： Tao Yue Si-Yi Chen Wen-Kang Shen Zhan-Ye Zhang Liming Cheng An-Yuan Guo Center for Artificial Intelligence Biology Hubei Bioinformatics&Molecular Imaging Key LaboratoryCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Laboratory Medicine Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune *** of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and *** TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced ***,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and *** combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as *** TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 *** is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://***/TCRosetta/.

关键词： T-cell receptor TCR repertoire analysis Batch search Annotation Web server.

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COCL: Cluster Combined with Outlier Contrast Learning for Unsupervised Person Re-Identification 9

COCL: Cluster Combined with Outlier Contrast Learning for Un...

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9th IEEE Smart World Congress, SWC 2023

作者： Li, Zhiqiang Li, Yidong Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

ISBN: (纸本)9798350319804

Unsupervised person re-identification (re-ID) is a challenging task which has received extensive attention from the industry. State-of-the-arts unsupervised re-ID methods adopt clustering techniques to generate pseudo labels and train re-ID models progressively with InfoNCE loss. However, they discard the clustering outlier samples during training, which also carry valuable information. In this paper, we propose a new approach making reasonable use of outlier samples, and optimize feature representation at the cluster level. Moreover, we find that using a single memory dictionary update strategy throughout the entire training process is unreasonable. To address this issue, we propose a two-stage training framework. At each stage, we choose the optimal memory dictionary update strategy, which greatly facilitates training. Our method mainly focuses on downstream tasks of unsupervised re-ID and extensive experiments on two popular large-scale re-ID benchmarks demonstrate that our proposed Cluster Combined with Outlier Contrast Learning(COCL) outperforms most unsupervised methods by a significant margin. © 2023 IEEE.

关键词： contrastive learning outlier person re-ID unsupervised learning

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Design of a Virtual Simulation Experimental Teaching Platform Based on the Internet of Things 13

Design of a Virtual Simulation Experimental Teaching Platfor...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Xiehua, Yu Shengjie, You Rentao, Lin School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The virtual simulation experiment teaching system based on the Internet of Things simulates the Internet of Things sensing device in the form of software, and integrates relatively new teaching methods and theoretical knowledge, designs and completes a characteristic, can adapt to various needs of the Internet of Things experiment teaching system, and strives to provide more efficient protection and help for teachers' teaching tasks and students' learning tasks. To a large extent, it can solve the problems in the design and implementation of the Internet of Things application system, provide students with practical operation opportunities in the way of virtual simulation, combine theory and practice, and cultivate application-oriented talents urgently needed by the industry. © 2023 IEEE.

关键词： Internet of things

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation 38

Generated and Pseudo Content guided Prototype Refinement for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Lili Lang, Congyan Chen, Ziyi Wang, Tao Li, Yidong Liu, Jun School of Computer Science & Technology Beijing Jiaotong University China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computing and Communications Lancaster University United Kingdom

Few-shot 3D point cloud semantic segmentation aims to segment query point clouds with only a few annotated support point clouds. Existing prototype-based methods learn prototypes from the 3D support set to guide the segmentation of query point clouds. However, they encounter the challenge of low prototype quality due to constrained semantic information in the 3D support set and class information bias between support and query sets. To address these issues, in this paper, we propose a novel framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR), which explicitly leverages LLM-generated content and reliable query context to enhance prototype quality. GPCPR achieves prototype refinement through two core components: LLM-driven Generated Content-guided Prototype Refinement (GCPR) and Pseudo Query Context-guided Prototype Refinement (PCPR). Specifically, GCPR integrates diverse and differentiated class descriptions generated by large language models to enrich prototypes with comprehensive semantic knowledge. PCPR further aggregates reliable class-specific pseudo-query context to mitigate class information bias and generate more suitable query-specific prototypes. Furthermore, we introduce a dual-distillation regularization term, enabling knowledge transfer between early-stage entities (prototypes or pseudo predictions) and their deeper counterparts to enhance refinement. Extensive experiments demonstrate the superiority of our method, surpassing the state-of-the-art methods by up to 12.10% and 13.75% mIoU on S3DIS and ScanNet, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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ASADA: Attention-Induced Style Alignment Domain Adaptation for Traffic Object Detection 9

ASADA: Attention-Induced Style Alignment Domain Adaptation f...

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9th IEEE Smart World Congress, SWC 2023

作者： Mao, Xiao Liu, Zining Zhang, Hui Li, Yidong Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

ISBN: (纸本)9798350319804

The development of deep learning models for intelligent vehicles rely on a large number of reliable data, among which large-scale and accurately labeled traffic scene image data is conducive to promoting the research of intelligent perception algorithms. In contrast to the time-consuming and laborious process of manual annotation, the virtual dataset synthesis method is a convenient and efficient way to replace manual labeling. However, due to the existence of automatically labeled samples in the virtual dataset that are not conducive to model training, and the domain shift between the virtual dataset and the real scene, the model trained from the virtual dataset cannot be well generalized to the real scene. To address these issues, we propose a two-stage label screening strategy for the data processing to remove the interference samples with low information content and further propose a novel efficient traffic scene dataset named Large-Scale Software-Defined Transportation Virtual dataset (LSTVD). Moreover, an attention-induced style alignment domain adaptation method (ASADA) is proposed to reduce the distribution difference between the virtual data domain and the real-world data domain. Experiment results demonstrate the efficiency of our label screening strategy and our domain adaptation method. © 2023 IEEE.

关键词： Cross-domain Object Detection Label Screening Strategy Virtual dataset

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