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arXiv 2024年

作者： Wang, Zihan Xiong, Ziliang Tang, Hongying Yuan, Xiaobing Shanghai Institution of Micro-system and Information Technology University of Chinese Academy of Science China Computer Vision Laboratory Department of Electrical Engineering LiU Sweden School of Information Science and Technology Shanghai Tech University China

Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Automating smart Internet of Things devices in modern homes using context-based fuzzy logic

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Computational Intelligence 2024年第1期40卷

作者： Ming, Chen Kadry, Seifedine Dasel, A. Antony Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang China School of Automotive and Traffic Engineering Hubei University of Arts and Science Xiangyang China Department of Mathematics and Computer Science Beirut Arab University Beirut Lebanon Department of Mechanical Engineering VIT University Vellore India

Internet of Things (IoT) is mainly used to connect different embedded objects over the internet to make communication between them possible. With the help of IoT, devices find a way to interact, work together, and study from each other's experiences just like humans do. IoT finds its way in applications such as smart home, smart city, healthcare, agriculture, and so on. The name smart home arises due to the automation of the normal home appliances to make it smart. When the devices of the normal smart home are connected via the internet, they become a part of the IoT. The smart home should ensure the following characteristics such as security, comfort, convenience, and energy saving. The article presents a technique for IoT controlled devices in a smart home using context-based fuzzy logic. Fuzzy logic is mainly used to monitor and analyze the real-time data collected from the sensors in the smart homes from various environments. Context-based fuzzy logic uses a multivalued logic principle which differs from the normal Boolean logic, where the truth value lies between only zero and one (ie, true or false). The proposed smart home is implemented in a real case scenario where it yields an accuracy of 90.5%, response time of 6.41 milliseconds, and an F-measure of 97%. © 2020 Wiley Periodicals LLC.

关键词： Internet of things

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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Camouflaged Object Tracking: A Benchmark

arXiv

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arXiv 2024年

作者： Guo, Xiaoyu Zhong, Pengzhi Zhang, Hao Huang, Defeng Shao, Huikai Zhao, Qijun Li, Shuiwang The College of Computer Science and Engineering Guilin University of Technology 541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology 541004 China The School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The College of Computer Science Sichuan University Sichuan610065 China

Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. This challenge is particularly critical in applications such as military, security, agriculture, and marine monitoring, where precise tracking of camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://***/openat25/ HIPTrack-MLS. © 2024, CC BY-NC-ND.

关键词： Object detection

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Up to 320-GBaud Coherent Optical system Based on Broadband Segmented TFLN I/Q Modulator

Up to 320-GBaud Coherent Optical System Based on Broadband S...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Lingjun Zhou Xiansong Fang Yixiao Zhu Junbo Zhu Yunchen Li Xiaomin Nie Xian Zhou Chuanchuan Yang Lei Wang Weisheng Hu Ke Li Zhixue He Fan Zhang School of Electronic and Computer Engineering Peking University Shenzhen China Pengcheng Laboratory Shenzhen China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing China State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

We demonstrate the record 320-GBd single-polarization QPSK and 300-GBd dual-polarization QPSK signal generation and intradyne coherent detection. This result is achieved with a novel broadband segmented TFLN I/Q modul... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Partial response signaling Phase shift keying Optical recording Optical device fabrication Lithium Optical fiber communication Encoding Broadband communication Optical modulation Photonics

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Distillation for Object Detection 39

DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Dist...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Lin, Yang Guo, Hang Wang, Jinbao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

Knowledge distillation (KD) has recently gained great success in the field of object detection. By transferring the knowledge of the spatial or channel domain from the teacher model to the student model, it allows for a more compact representation with minimal performance loss. Despite this progress, existing KD methods typically treat knowledge from spatial or channel domains independently, ignoring the exploitation of the mutual relationship between these domains. In this work, we first explore the connection between spatial and channel domains and find there exists a strong correlation between them, i.e. the salient channels tend to contain significant object regions in the spatial domain. Motivated by this observation, we propose DCSF-KD, a novel Dynamic Channel-wise Spatial Feature Knowledge Distillation framework for object detection by fully exploiting both spatial and channel knowledge. Specifically, we introduce channel-wise spatial feature distillation and global channel attention distillation, using information from both domains to improve the accuracy of the student network. Experiments demonstrate that our DCSF-KD outperforms existing detection methods on both homogeneous and heterogeneous teacher-student network pairs. For example, when using the MaskRCNN-Swin detector as the teacher, and based on RetinaNet and FCOS with ResNet-50 on MS COCO, our DCSF-KD can achieve 41.9% and 44.1% mAP, respectively. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Design of Environment Monitoring system Based on Single-Chip Microcomputer

Design of Environment Monitoring System Based on Single-Chip...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Fan Wang Guojun Liu Wei Zhang Wen Wang School of Computer Science and Information Engineering Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education

This article proposes a real-time environment monitoring system based on single-chip microcomputer, which consists of the device and user. The user adopts WeChat small program, while the device includes STMicroelectronics (STM32) single-chip microcomputer (SCM) control module, temperature and humidity sensor module, illumination sensor module, smoke sensor module, Wireless Fidelity (WIFI) communication module, display module, alarm module, and CP2102 USB-TTL module. The device establishes the connection with the user through the WIFI communication module to achieve remote wireless transmission of data. This system can monitor physical environment information in real-time, not only improving comfort and office efficiency, but also achieving the purpose of preventing danger through preset alarms.

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks Data models

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Object Tracking by Jointly Utilizing Template Updating and Relocation Mechanisms⋆

SSRN

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SSRN 2023年

作者： Wen, Jia Ren, Kejun School of Information Science and Engineering Yanshan University Qinhuangdao066099 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province China

The Siamese network-based tracker has achieved competitive performance in the field of single target tracking because of its excellent tracking speed and tracking accuracy. When faced with target deformations, most Siamese trackers rely on fixed template features from the initial frame, severely constraining the tracker’s performance. To mitigate this situation, we introduce a novel tracking framework(SiamTAR) that allows adaptive template updates based on past tracking information. In addition, we design a feature refinement module which can capture the target information more accurately at the full scale on the search image through an integrated attention mechanism, thus enabling the tracker to pay less attention to redundant information. Notably, we introduce a relocalization mechanism to solve the problems of random drift of the tracking box and inaccurate scale estimation in online tracking. Extensive experiments on GOT-10K, UAV123, OTB100 and DTB70 datasets show that the tracker achieves good tracking results and real-time performance. © 2023, The Authors. All rights reserved.

关键词： Target tracking

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