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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3lab/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection

arXiv

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arXiv 2021年

作者： Deng, Jiajun Zhou, Wengang Zhang, Yanyong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China The Department of Computer Science University of Science and Technology of China Hefei230026 China

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H23D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed H23D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at https://***/djiajunustc/H-23D_R-CNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Tube Design of Integrated EDS Evacuated Tube Transportation system

Tube Design of Integrated EDS Evacuated Tube Transportation ...

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International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD

作者： Hong-Fu Shi Jun Zheng Yang Chen Shi-Cheng He School of Information Science and Technology Southwest Jiaotong University Chengdu China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu China Neijiang Power Supply Company Neijian China School of Electrical Engineering Southwest Jiaotong University Chengdu China

In this work, the superconducting electrodynamic suspension integrated with the evacuated tube is proposed. Taking the Maglev MLX01-02 as an exemplary design, the structure specifications of tube are determined based on electromagnetic interaction among the induced current generated in tube and onboard superconducting magnets. The results reveal that the radius and thickness of metal tube are customed to 3.5 m and 40 mm, adopting stainless steel. This work can newly guide design and application of evacuated tube transportation system.

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ViSpeak: Visual Instruction Feedback in Streaming Videos

arXiv

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arXiv 2025年

作者： Fu, Shenghao Yang, Qize Li, Yuan-Ming Peng, Yi-Xing Lin, Kun-Yu Wei, Xihan Hu, Jian-Fang Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Tongyi Lab Alibaba Group China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China China

Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent models due to its time-sensitive, omni-modal and interactive characteristics. In this work, we aim to extend the streaming video understanding from a new perspective and propose a novel task named Visual Instruction Feedback in which models should be aware of visual contents and learn to extract instructions from them. For example, when users wave their hands to agents, agents should recognize the gesture and start conversations with welcome information. Thus, following instructions in visual modality greatly enhances user-agent interactions. To facilitate research, we define seven key subtasks highly relevant to visual modality and collect the ViSpeak-Instruct dataset for training and the ViSpeak-Bench for evaluation. Further, we propose the ViSpeak model, which is a SOTA streaming video understanding LMM with GPT-4o-level performance on various streaming video understanding benchmarks. After finetuning on our ViSpeak-Instruct dataset, ViSpeak is equipped with basic visual instruction feedback ability, serving as a solid baseline for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Video streaming

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Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction

arXiv

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arXiv 2024年

作者： Jiang, Jianping Zhou, Xinyu Wang, Bingxuan Deng, Xiaoming Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand – the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://***/***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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3D Beamforming Based on Deep Learning for Secure Communication in 5G and beyond Wireless Networks

3D Beamforming Based on Deep Learning for Secure Communicati...

引用

2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yang, Helin Lam, Kwok-Yan Nie, Jiangtian Zhao, Jun Garg, Sahil Xiao, Liang Xiong, Zehui Guizani, Mohsen Nanyang Technological University Strategic Centre for Research in Privacy-Preserving Technologies and Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Ecole de Technologie Superieure Universite du Quebec Electrical Engineering Department Montreal Canada Xiamen University School of Informatics Xiamen China Singapore University of Technology and Design Pillar of Information System Technology and Design Singapore Qatar University Department of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9781665423908

Three-dimensional (3D) beamforming is a potential technique to enhance communication security of new generation networks such as 5G and beyond. However, it is difficult to achieve optimal beamforming due to the challenges of nonconvex optimization problem and imperfect channel state information (CSI). To tackle this problem, this paper proposes a novel deep learning-based 3D beamforming scheme, where a deep neural network (DNN) is trained to optimize the beamforming design for wireless signals in order to guard against eavesdropper under the imperfect CSI. With our approach, the system is capable of training the DNN model offline, and the trained model can then be adopted to instantaneously select the 3D secure beamforming matrix for achieving the maximum secrecy rate of the system, which is measured by the signal received by eavesdroppers outside the path of the beam. Simulation results demonstrate that the proposed solution outperforms the classical deep learning algorithm and 2D beamforming solution in terms of the secrecy rate and robust performance. © 2021 IEEE.

关键词： Beamforming

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Enhanced Multi-Scale Cross-Attention for Person Image Generation

arXiv

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arXiv 2025年

作者： Tang, Hao Shao, Ling Sebe, Nicu Van Gool, Luc The School of Computer Science Peking University Beijing100871 China Terminus AI Lab University of Chinese Academy of Sciences Beijing100049 China University of Trento Trento38123 Italy The Department of Information Technology and Electrical Engineering ETH Zurich Switzerland The Department of Electrical Engineering KU Leuven Belgium INSAIT Sofia Un. Bulgaria

In this paper, we propose a novel cross-attention-based generative adversarial network (GAN) for the challenging person image generation task. Cross-attention is a novel and intuitive multi-modal fusion method in which an attention/correlation matrix is calculated between two feature maps of different modalities. Specifically, we propose the novel XingGAN (or CrossingGAN), which consists of two generation branches that capture the person’s appearance and shape, respectively. Moreover, we propose two novel cross-attention blocks to effectively transfer and update the person’s shape and appearance embeddings for mutual improvement. This has not been considered by any other existing GAN-based image generation work. To further learn the long-range correlations between different person poses at different scales and sub-regions, we propose two novel multi-scale cross-attention blocks. To tackle the issue of independent correlation computations within the cross-attention mechanism leading to noisy and ambiguous attention weights, which hinder performance improvements, we propose a module called enhanced attention (EA). Lastly, we introduce a novel densely connected co-attention module to fuse appearance and shape features at different stages effectively. Extensive experiments on two public datasets demonstrate that the proposed method outperforms current GAN-based methods and performs on par with diffusion-based methods. However, our method is significantly faster than diffusion-based methods in both training and inference. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Joint high-resolution feature learning and vessel-shape aware convolutions for efficient vessel segmentation

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Computers in Biology and Medicine 2025年 191卷 109982-109982页

作者： Zhang, Xiang Zhu, Qiang Hu, Tao Guo, Song Bian, Genqing Dong, Wei Hong, Rao Lin, Xia Ling Wu, Peng Zhou, Meili Yan, Qingsen Mohi-ud-din, Ghulam Ai, Chen Li, Zhou College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Shaanxi Provincial Key Lab of Bigdata of Energy and Intelligence Processing School of Physics and Electronic Information Yanan University Yanan China School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China School of Software Nanchang University Nanchang China School of Cyber Security Zhejiang University Hangzhou China Department of Basic Education and Research Jiangxi Police College Nanchang China Northwestern Polytechnical University China

Clear imagery of retinal vessels is one of the critical shreds of evidence in specific disease diagnosis and evaluation, including sophisticated hierarchical topology and plentiful-and-intensive capillaries. In this work, we propose a new topology- and shape-aware model named Multi-branch Vessel-shaped Convolution Network (MVCN) to adaptively learn high-resolution representations from retinal vessel imagery and thereby capture high-quality topology and shape information thereon. Two steps are involved in our pipeline. The former step is proposed as Multiple High-resolution Ensemble Module (MHEM) to enhance high-resolution characteristics of retinal vessel imagery via fusing scale-invariant hierarchical topology thereof. The latter is a novel vessel-shaped convolution that captures the retinal vessel topology to emerge from unrelated fundus structures. Moreover, our MVCN of separating such topology from the fundus is a dynamical multiple sub-label generation via using epistemic uncertainty, instead of manually separating raw labels to distinguish definitive and uncertain vessels. Compared to other existing methods, our method achieves the most advanced AUC values of 98.31%, 98.80%, 98.83%, and 98.65%, and the most advanced ACC of 95.83%, 96.82%, 97.09%,and 96.66% in DRIVE, CHASE_DB1, STARE, and HRF datasets. We also employ correctness, completeness, and quality metrics to evaluate skeletal similarity. Our method's evaluation metrics have doubled compared to previous methods, thereby demonstrating the effectiveness thereof. © 2025 Elsevier Ltd

关键词： Ophthalmology

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Short-term train arrival delay prediction:a data-driven approach

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Railway sciences 2024年第4期3卷 514-529页

作者： Qingyun Fu Shuxin Ding Tao Zhang Rongsheng Wang Ping Hu Cunlai Pu Postgraduate Department China Academy of Railway SciencesBeijingChina Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail TransitBeijingChina Scientific and Technological Information Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Yibin Track Signal and Communication DepotChina Railway Chengdu Group Co.LtdChengduChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays ***,delay predictions often involve imprecise shifts based on known delay ***-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment *** current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen ***/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival *** combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information *** on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding ***-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late *** show precise and rapid *** with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late *** enhanced adaptability to real-world train operations,training with full data is ***/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay *** innovatively compares and analyzes the model’s performance using both full data and delay data ***,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstra

关键词： Train delay prediction Intelligent dispatching command Deep learning Convolutional neural network Long short-term memory Attention mechanism

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Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions

arXiv

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arXiv 2022年

作者： Xu, Kaiyuan Xiao, Wei Cassandras, Christos G. Division of Systems Engineering Center for Information and Systems Engineering Boston University BrooklineMA02446 United States Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology United States

We consider the merging control problem for Connected and Automated Vehicles (CAVs) aiming to jointly minimize travel time and energy consumption while providing speed-dependent safety guarantees and satisfying velocity and acceleration constraints. Applying the joint optimal control and control barrier function (OCBF) method, a controller that optimally tracks the unconstrained optimal control solution while guaranteeing the satisfaction of all constraints is efficiently obtained by transforming the optimal tracking problem into a sequence of quadratic programs (QPs). However, these QPs can become infeasible, especially under tight control bounds, thus failing to guarantee safety constraints. We solve this problem by deriving a control-dependent feasibility constraint corresponding to each CBF constraint which is added to each QP and we show that each such modified QP is guaranteed to be feasible. Extensive simulations of the merging control problem illustrate the effectiveness of this feasibility guaranteed controller. Copyright © 2022, The Authors. All rights reserved.

关键词： Quadratic programming

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