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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ruian He Shili Zhou Ri Cheng Yuqi Sun Weimin Tan Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Unmanned Aerial Vehicle (UAV) detection in the wild is a challenging task due to the presence of background noise and the varying size of the object. To address these obstacles, we propose a novel learning framework for robust UAV detectors, which we call Difference-based Multi-scale Learning (DML). We argue that motion information matters in UAV detection because of the low recognition in one frame. Our method utilizes the frame difference of multiple previous frames, extracting motion information and blocking background noise. We also fuse multiple spatial-temporal scales for training and inferencing, enabling fusion from different sources. In addition, to better evaluate the performance of UAV detection in different scales, we propose Multi-Scale Average Precision (MSAP) metric to aggregate the detection accuracy over multiple scales. Through extensive experiments, we demonstrate that our proposed approach improves the detection accuracy of baseline models. Notably, we achieve SOTA performance in the 3rd Anti-UAV Challenge, with 2nd place in Track 2 and 4th place in Track 1. 1

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Design and practice of virtual simulation of industrial Internet 4

Design and practice of virtual simulation of industrial Inte...

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4th International Conference on Advances in Computer Technology, information science and Communications, CTISC 2022

作者： Wu, Qingjin Zeng, Taisheng Zhu, Daxin Cai, Danlin Quanzhou Normal University Laboratory and Equipment Management Office Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou China Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Key Laboratory of Intelligent Computing and Information Quanzhou China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

In the cultivating new engineering talents of information discipline, people tend to adopt virtual simulation to simulate production line scenes of enterprises' intelligent manufacturing so as to solve the problem that students fail to go deep into the production lines of industrial fields for scene practice and that they can not collect, analyze and optimize data in the real intelligent manufacturing production lines. The innovative talent training methods which combine regional industries can be used to improve students' ability to analyze, design, debug, operate, maintain and optimize the intelligent manufacturing big data and to master methods and ideas for solving complex engineering problems with big data technology. They can also provide beneficial experience references for the training of new engineering talents of information discipline. © 2022 IEEE.

关键词： Big data

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An Accurate Visual Navigation Method for Wheeled Robot in Unstructured Outdoor Environment Based on Virtual Navigation Line

An Accurate Visual Navigation Method for Wheeled Robot in Un...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2021

作者： Liang, Zhen Fang, Tiyu Dong, Zihao Li, Jinping School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-Year Jinan250022 China

ISBN: (纸本)9789811669620

For the navigation problem in unstructured outdoor environment such as the field without artificial marks, a precise vision navigation method for wheeled robot based on virtual navigation line is proposed. Virtual navigation line is a virtual line based on a small amount of visual information, which can be used in many complex environments. Firstly, the robot needs to design the shape of the walking route. And the general robot’s walking routes can be divided into linear route and curve route. Secondly, different virtual navigation line strategies are generated for different walking routes. In the linear navigation part, the virtual navigation line is determined according to the tracking target and the reference target generated. In the curve navigation part, the curve virtual navigation line is fitted according to the multiple targets. Then, the virtual calibration line is determined based on the camera pitch angle, field of view (FOV) and the horizon position in the image, the offset angle and offset distance are computed by using the geometric relationship between virtual calibration line and virtual navigation line. Finally, the fuzzy PID control method is applied to correct robot's direction. The Pioneer3-DX robot is used to do experiments in the outdoor field. The results show that our method can make the robot walk along the designed route in the unstructured outdoor environment accurately, and the navigation accuracy is within 10 cm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Navigation

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Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoising

Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoisin...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chenyu Huang Weimin Tan Jiaxing Shi Zhen Xing Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ideal clean image. However, these methods ignore the effect of Aleatoric uncertainty in the noisy image (e.g., pixels deviating from the expected distribution). The presence of Aleatoric uncertainty causes degradation of the reconstructed target pixels, resulting in high uncertainty for these pixels (i.e., low confidence), which in turn leads to sub-optimal denoising results. To address this problem, we propose a novel uncertainty-aware unsupervised image denoising method named Uncer2Natural (U2N). It dynamically predicts the Aleatoric uncertainty for each noisy sample and produces satisfactory denoising results by reducing the effect of Aleatoric uncertainty. Extensive experimental results show that U2N outperforms state-of-the- art unsupervised image denoising methods in terms of both quantitative metrics and qualitative visual quality.

关键词： Degradation Visualization Uncertainty Art Noise reduction Signal processing Noise measurement

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Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition 41

Harnessing Hierarchical Label Distribution Variations in Tes...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Zhiyong Xu, Qianqian Wang, Zitai Li, Sicong Han, Boyu Bao, Shilong Cao, Xiaochun Huang, Qingming School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China BDKM University of Chinese Academy of Sciences China

This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the variation in these distributions can be broken down hierarchically into global and local levels. The global ones reflect a broad range of diversity, while the local ones typically arise from milder changes, often focused on a particular neighbor. Traditional methods predominantly use a Mixture-of-Expert (MoE) approach, targeting a few fixed test label distributions that exhibit substantial global variations. However, the local variations are left unconsidered. To address this issue, we propose a new MoE strategy, DirMixE, which assigns experts to different Dirichlet meta-distributions of the label distribution, each targeting a specific aspect of local variations. Additionally, the diversity among these Dirichlet meta-distributions inherently captures global variations. This dual-level approach also leads to a more stable objective function, allowing us to sample different test distributions better to quantify the mean and variance of performance outcomes. Theoretically, we show that our proposed objective benefits from enhanced generalization by virtue of the variance-based regularization. Comprehensive experiments across multiple benchmarks confirm the effectiveness of DirMixE. The code is available at https://***/scongl/DirMixE. Copyright 2024 by the author(s)

关键词： Benchmarking

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Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions

arXiv

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arXiv 2023年

作者： Sun, Yuqi He, Ruian Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis. In this paper, we propose the first novel interactive framework that utilizes human instructions to edit talking radiance fields to achieve personalized talking face generation. Given a short speech video, we first build an efficient talking radiance field and then apply the latest conditional diffusion model to edit images with human instructions during the optimization. To ensure audio-lip synchronization during the editing process, we propose a progressive dataset updating strategy to ensure successful editing while preventing distortion of lip shapes. We also introduce a lightweight refinement network for complementing image details and achieving controllable detail generation in the final rendered image. Our method enables real-time rendering at up to 30FPS on consumer hardware. Multiple metrics and user verification show that our approach significantly improves rendering quality compared to state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Rendering (computer graphics)

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Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation

arXiv

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arXiv 2023年

作者： Cheng, Ri Jiang, Xuhao He, Ruian Zhou, Shili Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision. In this paper, we develop an Uncertainty-Guided Spatial Pruning (UGSP) architecture to skip redundant computation for efficient frame interpolation dynamically. Specifically, pixels with low uncertainty indicate easy regions, where the calculation can be reduced without bringing undesirable visual results. Therefore, we utilize uncertainty-generated mask labels to guide our UGSP in properly locating the easy region. Furthermore, we propose a self-contrast training strategy that leverages an auxiliary non-pruning branch to improve the performance of our UGSP. Extensive experiments show that UGSP maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method achieves state-of-the-art performance with lower FLOPs on multiple benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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An Energy-Efficient Virtual Machine Placement Algorithm Based on Improved Grouping GA

An Energy-Efficient Virtual Machine Placement Algorithm Base...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Taisheng Zeng Xiaodong Wu School of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

Virtual machine placement (VMP) plays a crucial role in optimizing the efficiency and sustainability of data centers. By consolidating virtual machines (VMs) onto fewer physical machines (PMs), VMP can significantly reduce power consumption and enhance resource utilization. This not only leads to cost savings but also contributes to environmental sustainability by minimizing energy waste. This paper focuses on addressing the energy-efficient VMP problem, which is critical for modern data centers aiming to balance performance and energy consumption. To tackle this challenge, we propose an improved group-based genetic algorithm (GGA). The GGA employs a novel grouping approach to encode chromosomes, allowing for more effective representation of VM-to-PM assignments. Additionally, optimized crossover and mutation operators are designed to improve the algorithm's performance, ensuring faster convergence and better solutions. Experimental results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of energy-saving efficiency and convergence speed.

关键词： Data centers Power demand Virtual machines Energy efficiency Resource management Sustainable development Biological cells Optimization Genetic algorithms Convergence

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MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior

arXiv

引用

arXiv 2023年

作者： Zhou, Shili Jiang, Xuhao Tan, Weimin He, Ruian Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

In recent years, many deep learning-based methods have been proposed to tackle the problem of optical flow estimation and achieved promising results. However, they hardly consider that most videos are compressed and thus ignore the pre-computed information in compressed video streams. Motion vectors, one of the compression information, record the motion of the video frames. They can be directly extracted from the compression code stream without computational cost and serve as a solid prior for optical flow estimation. Therefore, we propose an optical flow model, MVFlow, which uses motion vectors to improve the speed and accuracy of optical flow estimation for compressed videos. In detail, MVFlow includes a key Motion-Vector Converting Module, which ensures that the motion vectors can be transformed into the same domain of optical flow and then be utilized fully by the flow estimation module. Meanwhile, we construct four optical flow datasets for compressed videos containing frames and motion vectors in pairs. The experimental results demonstrate the superiority of our proposed MVFlow, which can reduce the AEPE by 1.09 compared to existing models or save 52% time to achieve similar accuracy to existing models. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical flows

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