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Journal of Network Intelligence 2022年第1期7卷 59-69页

作者： Xu, Xiaoyu Cui, Jianfeng Chen, Xuhui Chen, Chin-Ling College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Internet of things application technology Xiamen361024 China Xiamen City University of Technology Xiamen361024 China School of Computer and Information Engineering Xiamen City University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

Facial expression recognition is a typical application of artificial intelligence. It has practical value and commercial prospects in application scenarios such as safe driving, medical assistance, online classrooms, and human-computer interaction. Traditional convolutional neural networks have low accuracy in facial expression recognition. To solve this problem, we propose a residual separable convolutional neural network, which combines depth-wise separable convolution with an improved residual network. When transmitting feature information, it fuses facial expression features through skip connection and utilizes global average pooling to reduce dimension, thus preventing gradient disappearance. We conduct experiments based on the data set FER2013. The results show that it can improve the accuracy of facial expression recognition, reduce model parameters and raise the utilization efficiency of the parameters effectively. © The Authors.

关键词： Convolution

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

arXiv

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arXiv 2023年

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 China

We present NNVISR - an open-source filter plugin for the VapourSynth1 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. © 2023, CC BY-SA.

关键词： Interpolation

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Who, What and Where: Composite-semantic Instance Search for Story Videos

Who, What and Where: Composite-semantic Instance Search for ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiahao Guo Chao Liang Zhongyuan Wang National Engineering Research Center for Multimedia Software (NERCMS) Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University

This paper studies Who-What-Where (3W) composite-semantic video instance search (INS) problem, which aims to find a specific person doing a queried action in a particular place. Mainstream approaches adopt a complete decomposition strategy, which divides a composite-semantic query into multiple single-semantic queries. However, due to the lack of necessary correlation analysis among constituent semantics, these methods cannot always generate identity-matching and semantics-consistent 3W INS results. To address the above challenges, we propose a partial decomposition scheme with action as the link. Specifically, we selectively split the 3W INS as person-action INS and action-location INS. The former ensures the retrieved person and action share the same identity by modeling their relative spatial positions at the frame level, while the latter improves the semantic consistency between action and location with a cross-semantic attention mechanism at the shot level. Particularly, we build a large-scale 3W INS dataset, containing over 470k video shots, on basis of NIST TRECVID 2016-2021 INS tasks and verify the effectiveness of the proposed method with both quantitative and qualitative experiments.

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Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adapt...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haoxiang Ma Ran Qin Modi Shi Boyang Gao Di Huang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We then propose a global-to-local alignment pipeline with individual global domain classifiers for scene features of RGB and depth images as well as a local one specifically working for grasp features in the two modalities. In particular, we propose a grasp prototype adaptation module, which aims to facilitate fine-grained local feature alignment by dynamically updating and matching the grasp prototypes from the simulation and real-world scenarios throughout the training process. Due to such designs, the proposed method substantially reduces the domain shift and thus leads to consistent performance improvements. Extensive experiments are conducted on the GraspNet-Planar benchmark and physical environment, and superior results are achieved which demonstrate the effectiveness of our method. Code is available at https://***/mahaoxiang822/GL-MSDA.

关键词： Training Codes Pipelines Prototypes Benchmark testing Robustness Robotics and automation

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An Efficient Lightweight Anonymous Authentication Scheme for Vehicle Platoons

An Efficient Lightweight Anonymous Authentication Scheme for...

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International Conference on computer and Communications (ICCC)

作者： Haiqiong Wu Jianhong Zhou Maode Ma School of Computer and Software Engineering Xihua University China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China China College of Engineering Qatar University Qatar

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the continuous advancement of 5G and autonomous driving technologies, platooning of vehicles is receiving increasing attention. This technology enables multiple vehicles to drive in coordinated formations, playing a significant role in addressing road safety, energy consumption, and environmental pollution issues. With this technology deployed, vehicle platoons face increasing challenges in secure communication and privacy protection. In addition, due to the high complexity, high dynamics, and limitations of computing resources of vehicle platoons, traditional anonymous authentication technology makes it difficult to provide vehicle platoons with more powerful, lighter, and more flexible anonymous authentication solutions. We therefore design a lightweight and efficient anonymous authentication scheme that significantly reduces reliance on trust institutions and roadside units compared to traditional car networking solutions. Comprehensive security and performance evaluations prove that our scheme cannot only meet the basic requirements of security and privacy, but also outperform existing technologies in computing and communication efficiency.

关键词： Performance evaluation Privacy Energy consumption Pollution Authentication Road safety Protection Vehicle dynamics Autonomous vehicles Faces

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Dual-demodulation large-scope high-sensitivity refractive index sensor based on twin-core PCF

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Optoelectronics Letters 2021年第4期17卷 193-198页

作者： LI Chen-yuan SONG Bin-bin WU Ji-xuan HUANG Wei WU Xu-jie JIN Chang The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 3003S7China

In this paper, a refractive index(RI) sensor based on the twin-core photonic crystal fiber(TC-PCF) is presented. Introducing the rectangular array in the core area makes the PCF possible to obtain high birefringence and low confinement loss over the wavelength range from 0.6 μm to 1.7 μm. Therefore, the core region can enhance the interaction between the core mode and the filling material. We studied theoretically the evolution characteristics of the birefringence and operating wavelength corresponding to the strongest polarization point under the condition of filling the rectangular array with RI matching fluid range from 1.33 to 1.41. Simulation results reveal that the proposed TC-PCF has opposite evolutions of change rates between the B and wavelength, and the maximum RI sensing sensitivities of 1.809× 10-2 B/RIU and 8 700 nm/RIU at low and high RI infill are obtained respectively, which means that the TC-PCF features of dual-parameter demodulation for the RI sensing can maintain a high refractive index sensing sensitivity within a large scope of RI ranging from 1.33 to 1.41. Compared with the results of single-parameter demodulation, it is an optimized method to improve the sensitivity of low refractive index sensors, which has great application potency in the field of biochemical sensing and detection.

关键词： polarization refractive index

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ADATS: Adaptive RoI-Align based Transformer for End-to-End Text Spotting

ADATS: Adaptive RoI-Align based Transformer for End-to-End T...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zepeng Huang Qi Wan Junliang Chen Xiaodong Zhao Kai Ye Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Scene text spotting has attracted great attention in recent years. Compared with two-stage approaches that locate scene texts in the first stage and recognize them in the second stage, the advantages of joint location and recognition training are not fully explored. In this paper, we present an ADaptive RoI-Align based transformer for end-to-end Text Spotting (ADATS), which simultaneously locates and recognizes text with a single forward pass. By employing an Adaptive RoI-Align, the text features are extracted from the feature extraction network with the original aspect ratio, such that less information is lost during the alignment of arbitrarily-shaped scene text. Attention-based segmentation and recognition heads allow us to simultaneously optimize detection and recognition. Experiments on ICDAR 2015, MSRA-TD500, Total-Text, and CTW1500 demonstrate the effectiveness of our method.

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Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

arXiv

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arXiv 2024年

作者： Ma, Haoxiang Qin, Ran Shi, Modi Gao, Boyang Huang, Di The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China The Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

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Knowledge Graph Driven Inference Testing for Question Answering software

Knowledge Graph Driven Inference Testing for Question Answer...

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International Conference on software engineering (ICSE)

作者： Jun Wang Yanhui Li Zhifei Chen Lin Chen Xiaofang Zhang Yuming Zhou State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Technology Soochow University China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

In the wake of developments in the field of Natural Language Processing, Question Answering (QA) software has penetrated our daily lives. Due to the data-driven programming paradigm, QA software inevitably contains bugs, i.e., misbehaving in real-world applications. Current testing techniques for testing QA software include two folds, reference-based testing and metamorphic testing. This paper adopts a different angle to achieve testing for QA software: we notice that answers to questions would have inference relations, i.e., the answers to some questions could be logically inferred from the answers to other questions. If these answers on QA software do not satisfy the inference relations, an inference bug is detected. To generate the questions with the inference relations automatically, we propose a novel testing method Knowledge Graph driven Inference Testing (KGIT), which employs facts in the Knowledge Graph (KG) as the seeds to logically construct test cases containing questions and contexts with inference relations. To evaluate the effectiveness of KGIT, we conduct an extensive empirical study with more than 2.8 million test cases generated from the large-scale KG YAGO4 and three QA models based on the state-of-the-art QA model structure. The experimental results show that our method (a) could detect a considerable number of inference bugs in all three studied QA models and (b) is helpful in retraining QA models to improve their inference ability.

关键词： computer bugs Knowledge graphs Programming software Question answering (information retrieval) Testing software engineering

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Design and implementation of unmanned aerial vehicle localization and light tracking system

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 67-74页

作者： Xu Ming Dong Chen Li Hanlu Luo Qiming Wang Lizi Zhang Wanru Liu Huixin School of Digital Media and Design Arts Beijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Unmanned aerial vehicles(UAV)are applied widely and profoundly in various ***,high-precision positioning and tracking in multiple scenarios are the core requirements for UAV *** ensure stable communication of UAVs in denial environments with substantial electromagnetic interference,a systematic solution is proposed based on a deep learning algorithm for target detection and visible light for UAV *** the cost and computational power limitations on the hardware,the you only look once(YOLO)v4-Tiny model is used for static target detection of the UAV *** UAV tracking,and a light tracker that can adjust the angle of emitted light and focus it on the target is used for dynamic tracking ***,achieving the primary conditions of UAV optical communication with good secrecy is also suitable for dynamic *** UAV tracker positions the UAV model by returning the coordinates and calculating the time delay,and then controls the spotlight to target the *** order to facilitate the deployment of deep learning models on hardware devices,the lighter and more efficient model is selected after *** trained model can achieve 99.25%accuracy on the test *** dynamic target detection can reach 20 frames per second(FPS)on a computer with an MX520 graphics processing unit(GPU)and 6 GB of random access memory(RAM).Dynamic target detection on a Jetson Nano can reach 5.4 FPS.

关键词： unmanned aerial vehicles deep learning light tracking target detection unmanned aerial vehicles location unmanned aerial vehicles tracking

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