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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Wang, Ke Huang, Weishan Chen, Yan Chen, Ningjiang He, Ziqi School of Computer and Electronic Information Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China

ISBN: (纸本)9789811579837

To meet the needs of transportation systems for smart scenic security services, real-time detection and identification of traffic anomalies with high accuracy is essential. Based on the multi-objective sparse optical flow estimation method based on KLT algorithm, an improved algorithm for robust sparse optical flow is designed. The Forward-Backward error calculation method was used to eliminate the error optical flow generated by the KLT algorithm and the robustness of optical flow was improved. The proposed algorithm was verified by the actual traffic scene monitoring example, and the anomaly detection accuracy is above 80%. Furthermore, it has good detection effect on the benchmark dataset. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optical flows

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A method of flavors and fragrances identification based on hybrid neural network

A method of flavors and fragrances identification based on h...

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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Zou, Yun Chen, Yan Zhou, Xiao Liang, Hailing China Tobacco Guangxi Industrial Co. Ltd. Guangxi Nanning530001 China School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Guangxi Key Laboratory of Multimedia Communication and Network Technology Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9781510675032

Traditional spices quality testing methods face problems such as long testing latency, high testing cost, complicated testing process, environment-dependent inconsistency and great influence of human factors on the testing results. In order to solve these problems, this paper proposes a hybrid model based on Shuffled Frog Leaping Algorithm (Shuffled Frog Leaping Algorithm, SFLA) and Back Propagation (Back Propagation, BP) neural network, namely SFLA-BP. The model first obtains the weights and biases as initial parameters through the BP neural network, then uses the SFLA to optimize the parameters, and finally feeds the optimized parameters back into the BP neural network for model training. Experiments show that the hybrid neural network model can effectively improve the accuracy of identifying fragrances and flavors, and reduce the interference of environmental and subjective factors, which has significant practical application value. © 2024 SPIE.

关键词： Neural networks

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Container Cluster Scheduling Strategy Based on Delay Decision Under Multidimensional Constraints 6th

Container Cluster Scheduling Strategy Based on Delay Decisio...

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Xue, Yijun Chen, Ningjiang Xie, Yongsheng School of Computer and Electronic Information Guangxi University Nanning53004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning53004 China

ISBN: (纸本)9789811579806

With the rise of online applications such as machine learning, stream processing, and interactive data-intensive applications in shared clusters, container cluster scheduling in data centers is facing new challenges. In order to solve the problem that application performance and economic cost cannot be balanced in a container cluster deploying a hybrid application, this paper proposes a container cluster scheduling strategy based on delay decision under multi-dimensional constraints. Formal language-based application placement constraints were introduced, and a task reorder model was established based on delayed decision-making. The experiments show that this strategy improves application performance and cluster utilization. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Containers

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Urban Scene Semantic Segmentation with Insufficient Labeled Data

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China communications 2019年第11期16卷 212-221页

作者： Qi Zheng Jun Chen Peng Huang Ruimin Hu National Engineering Research Center for Multimedia Software Computer School of Wuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation Center of Geospatial Technology Wuhan 430079China

Semantic segmentation of urban scenes is an enabling factor for a wide range of *** the development of deep learning in recent years,semantic segmentation tasks using high-capacity models have achieved considerable successes on large ***,the pixel-level annotation process,especially for urban scene images with various objects,is tedious and labor ***,the scale of the unlabeled data,which is currently easy to collect,is often much larger than labeled ***,using the abundant unlabeled data to make up the loss of the segmentation model from insufficient labeled data is of great *** this paper,we propose a semi-supervised method based on reinforcement learning to capture the contextual information from the unlabeled data to improve the model trained on the small scale labeled *** quantitative and qualitative experiments have shown the effectiveness of the proposed method.

关键词： semantic segmentation semi-supervised learning reinforcement learning

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Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

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China communications 2018年第12期15卷 208-219页

作者： Wenhua Fang Jun Chen Ruimin Hu National Engineering Research Center for Multimedia Software Computer School of Wuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation Center of Geospatial Technology Wuhan 430079China

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

关键词： attributes recognition CNN multi-task learning

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A Two-Stage Chinese Medical Video Retrieval Framework with LLM 12th

A Two-Stage Chinese Medical Video Retrieval Framework with ...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Lei, Ningjie Cai, Jinxiang Qian, Yixin Zheng, Zhilong Han, Chao Liu, Zhiyue Huang, Qingbao School of Electrical Engineering Guangxi University Guangxi Nanning China School of Computer Electronics and Information Guangxi University Guangxi Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China

ISBN: (纸本)9783031446986

With the increasing popularity of online videos, research on video corpus retrieval (VCR) has made significant progress. However, existing VCR models have not performed well in the medical field due to the unique characteristics of medical VCR task. Specifically, the open-ended queries used in medical VCR are more challenging compared to image-caption style queries, and the long duration of medical videos poses a great burden on model retrieval efficiency. To address these challenges, we propose a two-stage framework based on GPT-3.5 and cross-modal contrastive global-span (CCGS) for medical video VCR (termed GPT-CMR). In the first stage, we leverage the powerful natural language processing capabilities of the large language model (LLM) GPT-3.5 to improve retrieval efficiency. In the second stage, we use CCGS model to further enhance retrieval accuracy. Additionally, we developed a CCGS-VCR Analyzer to leverage the characteristics of the CCGS model’s output without additional training costs. According to the official result, our method achieve first place in Track 2 of the NLPCC 2023 Task 5 competition. Experiments show that our method has retrieval efficiency and accuracy far exceeding the official baseline. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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Certificateless Aggregate Signature Scheme with High Efficencicy in Vehicular Ad-hoc network 4

Certificateless Aggregate Signature Scheme with High Efficen...

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4th International Conference on Electronic Information technology and Computer Engineering, EITCE 2020

作者： Hu, Xiaoming Tan, Wenan Ma, Chuang Xu, Huajie College of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China

ISBN: (纸本)9781450387811

Due to the characteristic of Certificateless aggregate signature (CLASS) scheme, CLASS can be applied into Vehicular Ad-hoc network (VANETs) and other fields. In this paper, we present an improved CLASS scheme for VANETs. In the improved scheme, by using two level dummy identities (one level in the RTA and another level in the RSU), the vehicular can adopt different dummy identity in different RSU or even in the same RSU which can reduce the risk from exposure of one dummy identity and increase the safeguard of the real identity of the vehicular. The improved scheme can protect the real identity of the vehicular. But it is easy to trace the real identity of the vehicular which may break the traffic regulations or do other harmful things. We also give the security analysis of the improved scheme on the unforgeability, blindness and traceability. What's more, the improved scheme has high efficient. It only needs two pairing computation no matter how many signature is aggregated which is more efficient than other similar schemes. © 2020 ACM.

关键词： Vehicular ad hoc networks

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Robust Background Subtraction Method via Low-Rank and Structured Sparse Decomposition

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China communications 2018年第7期15卷 156-167页

作者： Minsheng Ma Ruimin Hu Shihong Chen Jing Xiao Zhongyuan Wang National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation Center of Geospatial Technology Wuhan 430079China

Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.

关键词： background subtraction LRSD structured sparse SILTP

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COO-DuDo: computation overhead optimization methods for dual-domain sparse-view CT reconstruction

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Expert Systems with Applications 2025年 286卷

作者： Zihan Deng Zhisheng Wang Yuanlin Shan Guohang He Tiantian Du Shunli Wang Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin 150080 China Key Lab of Ultra-Precision Intelligent Instrumentation (Harbin Institute of Technology) Ministry of Industry and Information Technology Harbin 150080 China Artificial Intelligence Media Computing Laboratory School of Computer Science and Technology Harbin Engineering University Harbin 150080 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning 530000 China Faculty of Education University of Hong Kong Hong Kong

Recently, deep learning methods have shown exciting effects in Sparse-view CT reconstruction. The Dual-Domain (DuDo) deep learning method is one of the representative methods, and it can process the information in both the sinogram and image domains. However, the existing DuDo methods do not pay enough attention to the allocation of training costs and strategies for the two domains, which will result in wasted computing overhead or insufficient training in one of the two domains. In this paper, we propose a Computation-Overhead Optimization (COO) DuDo training strategy for sparse-view CT reconstruction, i.e., COO-DuDo. The training ratio of different domains is controlled by calculating their computation overhead, loss, and gradient variation of the loss. To make our COO-DuDo strategy enable sparse-view CT reconstruction better, we adopt a DuDo-network (COO-DDNet) structure based on two coding-decoding-type subnetworks. The evaluation, animal and clinical experiments have verified the effectiveness of our training strategies and methods. Our research provides a broader perspective for dual-domain image restoration tasks from the perspective of computational overhead.

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3D mesh pose transfer based on skeletal deformation

3D mesh pose transfer based on skeletal deformation

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作者： Yang, Shigeng Yin, Mengxiao Li, Ming Li, Guiqing Chang, Kan Yang, Feng School of Computer Electronics and Information Guangxi University Guangxi Nanning China Guangxi Key Laboratory of Multimedia Communications Network Technology Guangxi University Guangxi China School of Computer Science and Engineering South China University of Technology Guangdong China

For 3D mesh pose transfer, the target model is obtained by transferring the pose of the reference mesh to the source mesh, where the shape and pose of the source are usually different from that of the reference. In this paper, pose transfer is considered as a deformation process of the source mesh, and we propose a 3D mesh pose transfer method based on skeletal deformation. First, we design a neural network based on the edge convolution operator to extract the skeleton of the 3D mesh and bind the rigid weights;then, we calculate the bone transformations between the two skeletons with different poses and use the diffusion equation to smooth the rigid weights;finally, the source mesh is deformed according to the bone transformations and the smooth weights to get the target mesh. Experiment results on different datasets show that the pose of the reference mesh can be effectively transferred to the source one while maintaining the shape and high-quality geometric details of the source mesh by using our method. © 2023 John Wiley & Sons Ltd.

关键词： Deep learning

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