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2009 International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2009

作者： Peng, Ming Zhang, Jiawen Li, Ming Hu, Yang He, Zhaohua School of Computer State Key Laboratory of Software Engineering Wuhan University China National Engineering Research Center for Multimedia Software Wuhan University China

ISBN: (纸本)9781615676576

The idea is intended for searching a wanted place in a foreign city with the business name, the scene text and the street address. There are two contributions in this paper. First the difficulty of locating the specified buildings exactly in an unfamiliar city is analyzed, and then a novel application associated with a mapping application to the real view of target place is presented in this paper. Another is to improve printed Chinese character classification with high accuracy. This paper conducts the problem to combine Euclidean distance and support vector machines. Euclidean distance is applied to rough classification stage, and then we apply support vector machines to classify characters that cannot be classified at rough classification stage.

关键词： Support vector machines

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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An efficient and fast polarity optimization approach for mixed polarity Reed-Muller logic circuits

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Frontiers of computer Science 2017年第4期11卷 728-742页

作者： Zhenxue HE Limin XIAO Fei GU Tongsheng XIA Shubin SU Zhisheng HUO Rong ZHANG Longbing ZHANG Li RUAN Xiang WANG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100191 China

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.

关键词： genetic algorithm polarity optimization mixed

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing 210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China China University of Petroleum (East China) College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Combinatorial auction distributed DNN inference dynamic scheduling edge-cloud networks resource pricing

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NNVISR: Bring Neural network Video Interpolation and Super Resolution into Video Processing Framework 24

NNVISR: Bring Neural Network Video Interpolation and Super R...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tong, Yuan Hu, Mengshun Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://***/tongyuantongyu/vs-NNVISR. © 2024 ACM.

关键词： Open systems

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Dynamic Shortest Path Monitoring in Spatial networks

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Journal of computer Science & Technology 2016年第4期31卷 637-648页

作者： Shuo Shang Lisi Chen Zhe-Wei Wei Dan-Huai Guo Ji-Rong Wen State Key Laboratory of Software Development Environment Beijing 100191 China Department of Computer Science China University of Petroleum Beijing 102249 China School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Beijing Key Laboratory of Big-Data Management and Analysis Methods Renmin University of China Beijing 100080 China Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowa- days and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks （DSPM query）. When a traveler aims to a des- tination, his/her shortest path to the destination may change due to two reasons： 1） the travel costs of some edges have been updated and 2） the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons： 1） how to maintain and reuse the existing computation results to accelerate the following computations, and 2） how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

关键词： shortest path dynamic spatial network spatial database location-based service

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Salient object detection in video based on dynamic attention center 17th

Salient object detection in video based on dynamic attention...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Shao, Mengling Hu, Ruimin Wang, Xu Wang, Zhongyuan Xiao, Jing Gao, Ge State Key Laboratory of Software Engineering Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9783319488950

Salient object detection in video has attracted enormous research efforts for its wide applicability. But there are still some issues in restraining the disturbance of background, which make it difficult to detect salient object in complex scenarios. Inspired by the hypothesis of center prior in image domain, we novelly introduced the concept of dynamic attention center in video. The distance between specific regions and this center is used as a weighting term to restrain the influence of background disturbance and obtain more accurate spatial and temporal saliency maps. Besides, we develop a dynamic fusion method to combine the temporal and spatial saliency map, leading to higher spatiotemporal consistency. The experiments on Freiburg-Berkeley Motion Segmentation Dataset show that our method outperforms several state-of-art methods on subjective visual perception and objective measurements. © Springer International Publishing AG 2016.

关键词： Object recognition

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A Deep Understanding Video Q&A System for Film Education in Acting Department

A Deep Understanding Video Q&A System for Film Education in ...

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2023 International Conference on Intelligent Education and Intelligent research, IEIR 2023

作者： Wu, Zhengqian Li, Ruizhe Guo, Jiahao Wang, Zhongyuan Liang, Chao Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science China National Engineering Research Center for Multimedia Software School of Computer Science China Wuhan University School of Computer Science China

ISBN: (纸本)9798350342895

Recently, advancements in artificial intelligence technology have greatly influenced the field of education, particularly in the area of intelligent homework assistance. However, current approaches are primarily designed for procedural and logical tasks and often lack comprehension abilities. This limitation is particularly evident when it comes to multi-hop and continuous tasks. To address this challenge, the integration of Large Language Model (LLM) has significantly enhanced the capability of AI systems to handle multi-hop and highly interconnected inputs. In this study, we focus on the learning needs of students in Acting Department, specifically their study of movies and the significance of classic movie videos in their learning process. However, assessing deep comprehension of classic movies poses its own challenges. To overcome these challenges, we develop a quiz system utilizing Knowledge Graphs (KG) and LLM to facilitate a deeper understanding of classic films. The generation of video quiz pairs is achieved through the use of Automatic Speech Recognition (ASR) technology, which leverages movie subtitles for question generation. For answering these questions, we employ techniques KG and LLM to process questions and retrieve corresponding answers. The proposed method achieves good performance in Deep Video Understanding (DVU) task of NIST TRECVID, demonstrating its effectiveness. © 2023 IEEE.

关键词： Motion pictures

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Fine-grained vehicle recognition in traffic surveillance 17th

Fine-grained vehicle recognition in traffic surveillance

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wang, Qi Wang, Zhongyuan Xiao, Jing Xiao, Jun Li, Wenbin State Key Laboratory of Software Engineering Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9783319488899

Fine-grained vehicle recognition in traffic surveillance plays a crucial part in establishing intelligent transportation system. The major challenge lies in that differences among vehicle models are always subtle. In this paper, we propose a part-based method combining global and local feature for fine-grained vehicle recognition in traffic surveillance. We develop a novel voting mechanism to unify the preliminary recognition results, which are obtained by using Histograms of Oriented Gradients (HOG) and pre-trained convolutional neural networks (CNN), leading to fully exploiting the discriminative ability of different parts. Besides, we collect a comprehensive public database for 50 common vehicle models with manual annotation of parts, which is used to evaluate the proposed method and serves as supportive dataset for related work. The experiments show that the average recognition accuracy of our method can approach 92.3%, which is 3.4%–7.1% higher than the state-of-art approaches. © Springer International Publishing AG 2016.

关键词： Vehicles

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Mobile person re-identification with a lightweight trident CNN

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Science China(Information Sciences) 2020年第11期63卷 293-297页

作者： Mingfu XIONG Dan CHEN Xiaoqiang LU Engineering Research Center of Hubei Province for Clothing Information School of Mathematics and Computer ScienceWuhan Textile University National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan University Center for OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision MechanicsChinese Academy of Sciences

Dear editor,Person re-identification (re-ID) aims to recognize or associate a person and time at non-overlapping physical locations after the person had been previously observed visually elsewhere. This technology is ... 详细信息

关键词： Smart Mobile Device Person Re-identification Trident CNN Smart City Video Surveillance

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