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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Youliang Zhang Wenxuan Liu Danni Xu Zhuo Zhou Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University of Technology National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current approaches in Group Activity Recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Inter-actions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality commu-nication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： computer vision Computational modeling Cause effect analysis Communication channels Activity recognition

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Mining World Indicators for Analyzing and Modeling the Development of Countries

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ACM/IMS Transactions on Data Science 2021年第4期2卷 1–27页

作者： Huang, Hong Chi, Mingyuan Song, Yu Jin, Hai The National Engineering Research Center for Big Data Technology and System Key Laboratory of Service Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The world indicators released by the World Bank or other organizations usually give the basic public knowledge about the world. However, separate and static index lacks the complex interplay among different indicators and thus cannot help us have an overall understanding of the world. To this end, we study the world indicators from a different angle. Firstly, we discover that there exist correlations between indicators either from a static view or from a dynamic view. Moreover, taking the trade and diplomatic relationships into consideration, we construct a multi-relational network to depict the interactions between different countries, and propose a Multiple Relations to Vector (MR2vec) model to study world indicators from a network perspective. The experimental results show the changes of world indicators are predictable with the proposed model, and our proposed MR2vec has wide adaptability in predicting multi-relation networks. © 2022 Copyright held by the owner/author(s).

关键词： Data mining

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Modified Digital Current Controller for High Precision Electric Motor Emulators

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IEEE Transactions on Power Electronics 2025年

作者： Wang, Zeshang Wang, Zhifu Sun, Qingle Zhang, Xinan Beijing Institute of Technology School of Mechanical Engineering National Engineering Research Center of Electric Vehicles Beijing100081 China University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia

The electric motor emulator (EME) represents a practical application of power hardware-in-the-loop (PHIL) technology, facilitating the testing of electric motor drive units (MDUs). Its main function is to replicate both the steady-state and dynamic current characteristics of electric motors. Typically, the current loop in the EME functions as the inner loop of that in the MDU, requiring a high control bandwidth to ensure superior dynamic response performance. This paper analyzes the limitations of the control strategy for the EME introduced in a recent study and proposes a modified digital complex vector controller (MDCVC) with better dynamic performance and enhanced disturbance rejection capability. First, an internal loop is introduced to replace the complex poles of the original platform with real poles, followed by the design of a current controller to achieve overshoot-free current tracking performance. Furthermore, to improve the disturbance suppression performance, an additional active damping term is incorporated into the control loop. As a result, the dynamic characteristics of the target electric motor are accurately reproduced. Comparative evaluation tests are carried out to validate the effectiveness of the proposed control scheme under various operating conditions. © 1986-2012 IEEE.

关键词： Traction motors

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LET-Net: locally enhanced transformer network for medical image segmentation

LET-Net: locally enhanced transformer network for medical im...

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作者： Ta, Na Chen, Haipeng Liu, Xianzhu Jin, Nuo College of Computer Science and Technology Jilin University Changchun130012 China College of Computer Hulunbuir University Hulunbuir021008 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China Southampton Business School University of Southampton SouthamptonSO17 1BJ United Kingdom

Medical image segmentation has attracted increasing attention due to its practical clinical requirements. However, the prevalence of small targets still poses great challenges for accurate segmentation. In this paper, we propose a novel locally enhanced transformer network (LET-Net) that combines the strengths of transformer and convolution to address this issue. LET-Net utilizes a pyramid vision transformer as its encoder and is further equipped with two novel modules to learn more powerful feature representation. Specifically, we design a feature-aligned local enhancement module, which encourages discriminative local feature learning on the condition of adjacent-level feature alignment. Moreover, to effectively recover high-resolution spatial information, we apply a newly designed progressive local-induced decoder. This decoder contains three cascaded local reconstruction and refinement modules that dynamically guide the upsampling of high-level features by their adaptive reconstruction kernels and further enhance feature representation through a split-attention mechanism. Additionally, to address the severe pixel imbalance for small targets, we design a mutual information loss that maximizes task-relevant information while eliminating task-irrelevant noises. Experimental results demonstrate that our LET-Net provides more effective support for small target segmentation and achieves state-of-the-art performance in polyp and breast lesion segmentation tasks. © 2023, The Author(s).

关键词： Image enhancement

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Discriminative Dimension Selection for Enhancing the Interpretability and Performance of Clustering Output

Discriminative Dimension Selection for Enhancing the Interpr...

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International Conference on Knowledge and Smart technology (KST)

作者： Hrang Kap Lian Kitsana Waiyamai Slavakis Konstantinos Choochart Haruechaiyasak Department of Electrical Engineering Kasetsart University Bangkok Thailand Department of Computer Engineering Kasetsart University Bangkok Thailand Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Group National Electronics and Computer Technology Center Bangkok Thailand

Discriminative Dimension Selection (DDS) has emerged as a powerful tool for identifying the most relevant features in high-dimensional datasets, enabling interpretable data analysis and visualization. This paper explores the application of DDS which utilizes overlapping clusters and dimensions to enhance the interpretability and performance of the K-means clustering algorithm. Our approach leverages the post-processing capabilities of K-means to selectively retain informative features and discard redundant or irrelevant ones. This refined feature set not only preserves the clustering performance of K-means but also enhances its interpretability and visualization. We demonstrate the effectiveness of our method using a variety of datasets and compare its performance against traditional K-means and the proposed method Overlap-resolved Clustering (ORC). We also test with multiple cluster validity indices such as the Silhouette Coefficient Score (SS), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). Our results consistently show that ORC produces better clustering results and enhances the interpretability of the datasets. This study highlights the potential of DDS as a valuable tool for improving the interpretability and visualization of high-dimensional data analysis using K-means clustering.

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PlaNet: Learning to Mitigate Atmospheric Turbulence in Planetary Images 39

PlaNet: Learning to Mitigate Atmospheric Turbulence in Plane...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Yifei Zhou, Chu Zhu, Chengxuan Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China National Institute of Informatics Japan National Key Lab of General AI School of Intelligence Science and Technology Peking University China

ISBN: (纸本)157735897X

Obtaining planetary images with good visual quality is not an easy task since they are usually degenerated by atmospheric turbulence during the imaging procedure. Existing atmospheric turbulence mitigation methods designed for conventional images cannot be applied to planetary images, since the objects on the Earth have totally different degeneration patterns to planets. Besides, in planetary imaging, photographers often capture as many frames as possible to reduce the noise level of planetary images, which requires the method designed for planetary images to support an arbitrary number of input frames. In this paper, we propose a vertical distance-aware turbulence simulation pipeline to synthesize realistic planetary images in accordance with their unique degeneration patterns at a large scale with affordable computational cost, and design a neural network to mitigate the turbulence with flexible input frames by adopting an edge-based supervision strategy to handle the background scarcity issue. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

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computers and Industrial engineering 2024年 195卷

作者： Mohammadian, Asghar Zarrabi, Houman Jabbehdari, Sam Masoud Rahmani, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Tehran Iran Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources. Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%. © 2024 Elsevier Ltd

关键词： Energy utilization

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Heart Rhythm O2

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Heart Rhythm O2 2025年第5期6卷 678-686页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors

arXiv

引用

arXiv 2024年

作者： Liang, Jinxiu Yu, Bohan Yang, Yixin Han, Yiming Shi, Boxin National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Software and Microelectronics Peking University China

Event cameras, mimicking the human retina, capture brightness changes with unparalleled temporal resolution and dynamic range. Integrating events into intensities poses a highly ill-posed challenge, marred by initial condition ambiguities. Traditional regression-based deep learning methods fall short in perceptual quality, offering deterministic and often unrealistic reconstructions. In this paper, we introduce diffusion models to events-to-video reconstruction, achieving colorful, realistic, and perceptually superior video generation from achromatic events. Powered by the image generation ability and knowledge of pretrained diffusion models, the proposed method can achieve a better trade-off between the perception and distortion of the reconstructed frame compared to previous solutions. Extensive experiments on benchmark datasets demonstrate that our approach can produce diverse, realistic frames with faithfulness to the given events. © 2024, CC BY-NC-ND.

关键词： Cameras

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation 11

Semantic Level Image Retrieval in Domain Adaptive Semantic S...

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Zhu, Siyu Zhou, Fenfen Li, Kai Wen, Haonan Liu, Minghao Computer School Beijing Information Science and Technology University 102206 No.55 Taihang RoadChangping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100027 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing100190 China Yuquan Road Shijingshan District Beijing100049 China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments. © 2024 The Authors.

关键词： Semantic Segmentation

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