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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Aerodynamic Coefficients Prediction via Cross-Attention Fusion and Physical-Informed Training 39

Aerodynamic Coefficients Prediction via Cross-Attention Fusi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yueqing Zhang, Peng Liu, Yushuang Zhao, Jianing Lin, Jie Chen, Yi State Key Laboratory of Aerodynamics Sichuan China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan China Sichuan Tianfu Fluid Big Data Research Center Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)157735897X

Aerodynamic coefficient prediction is pivotal in aircraft and vehicles’ design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computations and high costs. Nevertheless, this data-driven approach faces several critical challenges, which limit its further performance enhancement: i) The current research lacks a profound understanding of the complex interplay between the shape of an object and its aerodynamic characteristics. ii) The scarcity of high-quality aerodynamic data poses a significant barrier. The models trained on limited datasets lack generalization ability, struggling to accurately predict and adapt to diverse aerodynamic performance under new shapes or conditions. To overcome these challenges, we introduce an innovative framework that employs cross-attention to capture the intimate interplay between shape and flow conditions and allows for the direct utilization of pre-trained models on general shape datasets to mitigate the scarcity of aerodynamic data. Furthermore, to bolster the inference capabilities of this data-driven approach, we integrate physical information constraints into the model, leveraging them as guiding principles to enhance the model’s predictive power under unknown conditions. Experimental validation demonstrates that our proposed method performs excellently in multiple aerodynamic prediction tasks. This achievement brings a new technological breakthrough to the field of aerodynamic prediction and provides robust support for the design optimization of complex systems such as aircraft and vehicles. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Technological forecasting

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Rethinking the visual cues in audio-visual speaker extraction

arXiv

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arXiv 2023年

作者： Li, Junjie Ge, Meng Pan, Zexu Cao, Rui Wang, Longbiao Dang, Jianwu Zhang, Shiliang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China

The Audio-Visual Speaker Extraction (AVSE) algorithm employs parallel video recording to leverage two visual cues, namely speaker identity and synchronization, to enhance performance compared to audio-only algorithms. However, the visual front-end in AVSE is often derived from a pre-trained model or end-to-end trained, making it unclear which visual cue contributes more to the speaker extraction performance. This raises the question of how to better utilize visual cues. To address this issue, we propose two training strategies that decouple the learning of the two visual cues. Our experimental results demonstrate that both visual cues are useful, with the synchronization cue having a higher impact. We introduce a more explainable model, the Decoupled Audio-Visual Speaker Extraction (DAVSE) model, which leverages both visual cues. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

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Approaches to Address Occlusion and Small Target Challenges in Restaurant Rat Detection

Approaches to Address Occlusion and Small Target Challenges ...

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computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Yanqing Li Zhanxiang Lai Hanyu Lou Junhan He Yixin Zhong Wen Xin School of Public Health Xiamen University Xiamen China School of Marine Science and Technology Northwestern Polytechnical University Xi’an China School of Software Northwestern Polytechnical University Xi’an China School of Lilac Harbin Institute of Technology Harbin China School of Computer and Big Data Heilongjiang University Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

In this study, an improved target detection algorithm is proposed for the problem of rat detection in restaurant environments. The restaurant environment is characterized by obvious light changes, complex occlusion, and ground reflections, which bring more challenges to the accurate detection of rats. For this reason, we make the following improvements to the YOLOv5 algorithm. First, we introduce the Glare Suppression Module, which utilizes an adaptive feature correction method to reduce the interference of highly reflective areas such as oil and water stains on the ground on the detection model, and to improve the recognition ability of the rat target in low-contrast scenes. Secondly, combined with the Background Adaptive Modeling (BAM) technology, the stability and generalization ability of the model is improved by dynamically learning the background features of the restaurant environment to reduce the false detections caused by the activities of the restaurant personnel, changes in the tables and chairs, and other factors. The experimental results show that the improved YOLOv5 algorithm can effectively improve the precision and recall rate of rat detection, reduce the false detection rate, and ensure the real-time performance in the restaurant environment, which provides efficient and reliable technical support for restaurant hygiene monitoring and pest rat prevention and control.

关键词： YOLO Adaptation models Prevention and mitigation Rodents Rats Feature extraction Reflection Real-time systems Reliability Personnel

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Towards Making More Reliable Cardiotocogram data Prediction with Limited Expert Knowledge: Exploiting Unlabeled data with Semi-supervised Boosting Method 6th

Towards Making More Reliable Cardiotocogram Data Prediction ...

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6th International Conference on data Mining and big data, DMBD 2021

作者： Hong, Jiaming Qin, Chen Huang, Yun Zhou, Yin School of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Software College Jishou University Zhangjiajie China School of Computer Science Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9789811674754

Cardiotocography (CTG) is frequently used as a method of diagnosing fetal distress during pregnancy and delivery, including listening to Fetal Heart Rate (FHR) and monitoring Uterine Contractions (UC). Many scholars have contributed to classify CTG data through machine learning methods, intending to reduce the consuming-time and mistakes during obstetricians’ identification. In this study, we used repeated holdout cross-validation for data pre-processing and classified the CTG dataset as a normal class and pathological class. Sensitivity, specificity, accuracy, and AUC were involved to measure the performance of the models. According to the results of ASSEMBLE AdaBoost and AdaBoost, we can conclude that ASSEMBLE AdaBoost is robust in the classification of CTG data. Training with few labeled data and amounts of unlabeled data, ASSEMBLE AdaBoost achieved an accuracy of over 90%, which was superior to AdaBoost. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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A Simple Dashboard for OpenStack Horizon 2021

A Simple Dashboard for OpenStack Horizon

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2021 4th International Conference on Computing and big data

作者： Hongbin Wang Shaoxu Li Dan Deng Leixiao Li Jing Gao Jie Li College of Data Science and Application Inner Mongolia University of Technology China and Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center based on Big Data China College of Computer and Information Engineering Inner Mongolia Agricultural University China

ISBN: (纸本)9781450387194

OpenStack is an open source cloud computing management project, which allows flexible resources applying. The traditional OpenStack dashboard is inflexible, thus a simple dashboard is designed. The simple dashboard integrates keystone, horizon, grace, nova, neutron, swift and other components, communicates with the traditional OpenStack dashboard and runs in the form of docker container. We implemented simple dashboard on a small cloud platform. Compared with the traditional dashboard, the interface of simple dashboard is simpler, more flexible and easy to use.

关键词： Cloud computing Horizon OpenStack Simple dashboard

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A cross Transformer for image denoising

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Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

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A self-supervised CNN for image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Jiao, Tiancai Zuo, Wangmeng Zhang, Yanning Lin, Chia-Wen the School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China the Peng Cheng Laboratory Laboratory Guangdong Shenzhen518055 China the School of Computer Science Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu300 Taiwan

Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal techniques. In this paper, we propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN). SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. A heterogeneous U-Net architecture is used to extract more complementary structural information via simple components for image watermark removal. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal. Besides, a watermark dataset is conducted. Experimental results show that the proposed SWCNN is superior to popular CNNs in image watermark removal. Codes can be obtained at https://***/hellloxiaotian/SWCNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Cloud-based storage and computing for remote sensing big data:a technical review

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International Journal of Digital Earth 2022年第1期15卷 1417-1445页

作者： Chen Xu Xiaoping Du Xiangtao Fan Gregory Giuliani Zhongyang Hu Wei Wang Jie Liu Teng Wang Zhenzhen Yan Junjie Zhu Tianyang Jiang Huadong Guo Key Lab of Digital Earth Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute for Environmental Sciences/GRID-Geneva University of GenevaGenevaSwitzerland Institute for Marine and Atmospheric research Utrecht (IMAU) Utrecht UniversityUtrechtThe Netherlands State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China Tianyi Cloud People’s Republic of China Telecom Co.LtdGuangzhouPeople’s Republic of China Department of Computer Science and Technology Tsinghua UniversityBeijingPeople's Republic of China

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and *** the progress of computer technologies,conventional computing implementations have become technically inefficient for processing *** computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past *** study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer ***,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,*** storage,metadata management,data homogeneity,and computing ***,we introduce state-of-the-art cloud-based data management technologies for RSBD *** unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data *** data models are suggested,***,ARD,data cubes,and composite ***,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing ***,we categorize the architectures of mainstream RSBD *** research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

关键词： Remote sensing big data cloud computing data cube analysis ready data parallel computing data model

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Efficient Pre-trained Semantics Refinement for Video Temporal Grounding

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Ao Liu, Huijun Zhu, Yiqing Ge, Yongxin Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing 400044 China Chongqing University School of Big Data and Software Engineering Chongqing 401331 China Chongqing University College of Computer Science Chongqing 401331 China Chongqing University National Elite Institute of Engineering Chongqing 401331 China

Video Temporal Grounding (VTG) is a fine-grained video understanding task that aims to ground the relevant video moments corresponding to given language queries. Most existing approaches utilize powerful Vision-Language Models (VLMs), augmented with additional network architectures or specialized modules to supplement temporal reasoning capabilities. Despite achieving impressive performance, these approaches tend to overlook a critical issue that pre-trained visual and textual representations are not specifically optimized for VTG. Particularly, such representations often suffer from inter-modal semantic misalignment referring to the inconsistency between different modalities, and intra-modal semantic confusion, caused by insufficient discriminability within visual modality. To address these limitations, we propose an efficient semantics refinement framework built upon pre-trained models, featuring two core components. First, the Modal Knowledge Bidirectional Propagation (MKBP) component promotes inter-modal semantic alignment via bidirectional enrichment of textual and visual semantics, exploiting their complementary strengths without introducing additional parameters. Second, the Content Context Contrast Learning (C3L) component alleviates intra-modal semantic confusion within visual modality by bringing query-specific visual features closer while separating irrelevant ones. Comprehensive experiments on six benchmark datasets demonstrate the superior performance of our proposed methods. © 1991-2012 IEEE.

关键词： Contrast learning Knowledge propagation Semantic refinement Video temporal grounding Vision-Language Models

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