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arXiv 2024年

作者： Li, Changhao Li, Haoling Xue, Mengqi Fang, Gongfan Zhou, Sheng Feng, Zunlei Wang, Huiqiong Song, Mingli Song, Jie School of Software Technology Zhejiang University Hangzhou China School of Computer and Computing Science Hangzhou City University Hangzhou China College of Design and Engineering National University of Singapore Singapore Ningbo Innovation Center Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China

Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics, leaving the progress in this area not fully comprehended. To fill this gap, we present the first comprehensive benchmark, termed PruningBench, for structural pruning. PruningBench showcases the following three characteristics: 1) PruningBench employs a unified and consistent framework for evaluating the effectiveness of diverse structural pruning techniques;2) PruningBench systematically evaluates 16 existing pruning methods, encompassing a wide array of models (e.g., CNNs and ViTs) and tasks (e.g., classification and detection);3) PruningBench provides easily implementable interfaces to facilitate the implementation of future pruning methods, and enables the subsequent researchers to incorporate their work into our leaderboards. We provide an online pruning platform http://*** for customizing pruning tasks and reproducing all results in this paper. Leaderboard results can be available on https://***/HollyLee2000/PruningBench. © 2024, CC BY.

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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

作者： Munkhjargal Gochoo Munkh-Erdene Otgonbold Erkhembayar Ganbold Jun-Wei Hsieh Ming-Ching Chang Ping-Yang Chen Byambaa Dorj Hamad Al Jassmi Ganzorig Batnasan Fady Alnajjar Mohammed Abduljabbar Fang-Pang Lin Department of Computer Science and Software Engineering United Arab Emirates University UAE Emirates Center for Mobility Research United Arab Emirates University UAE College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan University at Albany — State University of New York NY USA Department of Computer Science National Yang Ming Chiao Tung University Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.

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Imputation for Missing Items in a Stream Data Based on Gamma Distribution 5th

Imputation for Missing Items in a Stream Data Based on Gamma...

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5th International Conference on Smart computing and Communication, SmartCom 2020

作者： Sun, Zhipeng Zeng, Guosun Ding, Chunling Department of Computer Science and Technology Tongji University Shanghai200092 China Tongji Branch National Engineering and Technology Center of High Performance Computer Shanghai200092 China College of Chemical Science and Engineering Tongji University Shanghai200092 China

ISBN: (纸本)9783030747169

During the collection of real-time data, data missing is a common phenomenon in some stream data, which leads to the difficulty of such stream analysis and knowledge mining. In this paper, we study an approach of data imputation based on gamma distribution, which determines the number of missing data items in each time interval and the values of missing data items in a stream. We also present some metrics, such as fitting degree, credibility, matching degree, to evaluate the effectiveness of data imputation. Experimental results show that our approach improves the credibility by 15.0% to 20.0% compared to EMI, a widely adopted approach of data imputation, and outperforms traditional techniques significantly. © 2021, Springer Nature Switzerland AG.

关键词： Probability density function

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Localization of Image Splicing Under Segment Anything Model With Integrated Compression and Edge Artifacts

Localization of Image Splicing Under Segment Anything Model ...

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IEEE International Conference on Image Processing

作者： Ruhao Zhao Xian Zhong Liang Liao Wenxuan Liu Wenxin Huang Zheng Wang School of Computer Science and Artificial Intelligence Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology School of Electrical and Electronic Engineering Rapid-Rich Object Search Lab Nanyang Technological University College of Computing and Data Science Nanyang Technological University School of Computer Science and Information Engineering Hubei University School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated Compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM’s object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and Compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Location awareness Image segmentation Image coding Social networking (online) Image edge detection Splicing Feature extraction

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Reinforcing Spatiotemporal Correlation: Learning Comprehensive Feature Representations for Gait Recognition

SSRN

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SSRN 2024年

作者： Chen, Jianyu Wang, Zhongyuan Zeng, Kangli Zou, Qin Liang, Chao Xu, Zengmin Wu, Gang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China College of Information Engineering Tarim University Alar843300 China Shenzhen518060 China

Gait recognition attracts extensive attention as biotechnology for identifying subjects from a distance without their cooperation. Since gait is not easily camouflaged and successive motion trajectories facilitate the identification of subjects, capturing critical temporal information is particularly important for gait recognition. Compared with architectures that extract temporal features individually and then perform late fusion, 3D CNNs are more suitable for extracting continuous relationships between frames in gait sequences. Hence, in this paper, we propose a novel 3D architecture called robust spatiotemporal 3D convolutional neural network (RST3D) to collaboratively extract spatiotemporal correlations in gait sequences. Specifically, we propose a full-scale 3D convolutional block that fully considers the orthogonal relationships in multiple dimensions based on the original 3D convolution, thus it can extract more potential gait spatiotemporal features. Next, to further enhance the temporal modeling capability of the network, we propose a temporal pyramidal attention block to capture the most discriminative motion representations from different ranges of gait silhouettes. Experiments on CASIA-B, OUMVLP, GREW and Gait3D datasets demonstrate that our proposed method outperforms the state-of-the-art gait methods. Furthermore, compared to the existing 3D CNN-based gait recognition methods, our approach exhibits superior performance in several complex gait conditions. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Fisheye Multiple Object Tracking by Learning Distortions Without Dewarping

Fisheye Multiple Object Tracking by Learning Distortions Wit...

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IEEE International Conference on Image Processing

作者： Ping-Yang Chen Jun-Wei Hsieh Ming-Ching Chang Munkhjargal Gochoo Fang-Pang Lin Yong-Sheng Chen Department of Computer Science National Yang Ming Chiao Tung University Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Taiwan Department of Computer Science University at Albany State University of New York USA College of Information Technology United Arab Emirates University UAE Emirates Center for Mobility Research United Arab Emirates University UAE National Center for High-Performance Computing Taiwan

We develop a new Multiple Object Tracking (MOT) scheme for fisheye cameras that can directly perform vehicle detection, re-identification, and tracking under fisheye distortions without explicit dewarping. Fisheye cameras provide omnidirectional coverage that is wider than traditional cameras, reducing fewer need of cameras to monitor road intersections. However, the problem of distorted views introduces new challenges for fisheye MOT. In this paper, we propose a Fish-Eye Multiple Object Tracking (FEMOT) approach with two novelties. We develop the Distorted Fisheye Image Augmentation (DFIA) method to improve object detection and re-identification on fisheye cameras, where fisheye model training can be performed on existing datasets of traditional cameras via fisheye data synthesis and augmentation. We also develop the Hybrid Data Association (HDA) method to perform tracking directly on fisheye views, without the need of de-warping. The developed FEMOT framework provides practical design and advancement that enables large-scale use of fisheye cameras in smart city and surveillance applications.

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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Diagnosis of Covid-19 Via Multi-Layer Multi-center Graph Attention Network

Diagnosis of Covid-19 Via Multi-Layer Multi-Center Graph Att...

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IEEE International Symposium on Biomedical Imaging

作者： Pingkang Li Haijun Lei Xuegang Song Jia Zhao Jialan Tang Yukang Lei Baiying Lei Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China

Coronavirus disease in 2019 (COVID-19) is a global epidemic, which has affected more than billions of people worldwide. Its intelligent diagnosis based on imaging data has attracted lots of attention, but the heterogeneity between datasets renders its diagnosis challenging. To solve this problem, we propose a new multi-layer multi-center graph attention network (MM-GAN) for COVID-19 diagnosis based on computed tomography (CT) data. First, we use the 3D backbone network to extract feature. Second, we construct a multi-layer multi-center map by using extracted features and auxiliary information, which takes into account the heterogeneity between centers. Third, we use graph attention to generate a new graph structure and learn node representation. Finally, we input the multi-layer multi-center graph into the graph convolution to achieve the COVID-19 detection. Experiments on four multi-center datasets show that the framework is effective and outperforms the traditional classification methods.

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Synergizing Symbolic Execution and Fuzzing By Function-level Selective Symbolization

Synergizing Symbolic Execution and Fuzzing By Function-level...

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Asia-Pacific Conference on software engineering

作者： Guofeng Zhang Zhenbang Chen Ziqi Shuai Yufeng Zhang Ji Wang College of Computer National University of Defense Technology ChangSha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781665455381

Constraint solving and environment modeling are two challenging problems for symbolic execution. When a program contains non-linear expressions, it is difficult for symbolic execution to explore the program’s whole path space due to the high complexity of the constraint solving for the nonlinear constraints. Besides, when the program uses a third-party library and the source code of the library is not available, the symbolic execution of the program often under-approximates the analysis by concrete execution or over-approximates by introducing new symbolic variables, which may fail to explore the whole path space or introduce false alarms, respectively. This paper proposes FUSE, a framework of synergizing symbolic execution and fuzzing by function-level selective symbolization to tackle these problems. First, FUSE collects the path constraints of each function selectively and introduces symbolic function invocation expressions for the complex or third-party functions. Then, FUSE combines SMT solving and fuzzing to solve the path constraints. We have implemented FUSE on the start-of-theart symbolic execution engine KLEE. The experimental results demonstrate that FUSE effectively and efficiently improves the code coverage. Compared with the state-of-the-art, FUSE achieves 6. 6x speedups for achieving the same code coverage.

关键词： Codes Fuses Source coding Semantics Fuzzing Benchmark testing Libraries

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