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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm

arXiv

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arXiv 2023年

作者： Li, Jinli Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

A high-dimensional and incomplete (HDI) matrix can describe the complex interactions among numerous nodes in various big data-related applications. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm learns a latent factor relying on the stochastic gradient of current instance error only without considering past update information. To address this critical issue, this paper innovatively proposes a Fuzzy PID-incorporated SGD (FPS) algorithm with two-fold ideas: 1) rebuilding the instance learning error by considering the past update information in an efficient way following the principle of PID, and 2) implementing hyper-parameters and gain parameters adaptation following the fuzzy rules. With it, an FPS-incorporated LFA model is further achieved for fast processing an HDI matrix. Empirical studies on six HDI datasets demonstrate that the proposed FPS-incorporated LFA model significantly outperforms the state-of-the-art LFA models in terms of computational efficiency for predicting the missing data of an HDI matrix with competitive accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Study on Improved YOLOv7-based Algorithm for Detecting Crack Defects in Ancient Buildings 24

Study on Improved YOLOv7-based Algorithm for Detecting Crack...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P Xie, Zhiying Yang, Aihua He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment Dongting Lake Area Changsha Hunan410004 China

ISBN: (纸本)9798400716942

In the protection and restoration work of ancient buildings, the detection and analysis of the cracks and defects of the exterior walls of ancient buildings is a crucial task. In this paper, an improved YOLOv7 algorithm is proposed to detect the defects of ancient external walls, aiming to achieve efficient and accurate detection of the defects of ancient external walls. The method involves preprocessing the image using a rectangle clipping algorithm, graying the image using the maximum method, and denoising the image with a median filter. Next, the image is labeled using the LabelImg tool, and the data is split into training, validation, and test sets. During model training, a CBAM-Net module is inserted after several CBS modules in the backbone network, the GIOU loss function is replaced by CIOU, and feature task extraction is performed. The realized attention mechanism is added to the Yolov7 model. The results show that the mAP value of crack detection after the improved algorithm reaches 81.2%. In contrast to the initial YOLOv7 algorithm, the precision of the model has been boosted to 87.2%, exhibiting a 6.0% enhancement in accuracy and an 85.2% improvement in recall rate. The improved YOLOv7 crack detection algorithm reduces network calculation and improves crack classification and recognition ability. This approach is crucial in safeguarding ancient buildings as it enables varying levels of maintenance depending on the severity of cracks. © 2024 ACM.

关键词： Attention mechanism Deep learning Defect detection Image processing YOLOv7

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Group RandAugment: Video Augmentation for Action Recognition 5

Group RandAugment: Video Augmentation for Action Recognition

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5th International Conference on data Science and Information Technology, DSIT 2022

作者： An, Fengmin Zhang, Bingbing Wang, Zhenwei Dong, Wei Zhang, Jianxin School of Computer Science and Engineering Dalian Minzu University Dalian China Institute of Machine Intelligence and Bio-computing Dalian Minzu University Dalian China SEAC Key Lab of Big Data Applied Technology Dalian Minzu University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

data augmentation, as a critical strategy in deep learning, well improves the sample diversity for network training, leading to the obvious improvement of model generalization ability. Besides, automatic data augmentation, while sparking in image tasks, has attracted too little attention in video recognition task. Therefore, this work explores a novel group random augmentation (GRA) that automatically augments video data for recognition. GRA first collects 21 augmentation transformations to enrich the sample diversity, and then divides these transformations into four groups (i.e., pixel-transform, rigidtransform, erasing-transform, environment-transform) to reduce the excessive regularization caused by similar augmentation. To reduce the computational cost of finding the optimal setting, GRA adopts the combination form to select the optimal situation. For frames in the same video clip, GRA uses synchronous GRA. Additionally, the proposed GRA can be integrated into any existing video frameworks. To prove the effectiveness of GRA, we conduct experiments on two commonly used video action recognition benchmarks (Something-Something V1 & V2) and three typical frameworks (TSM, GSM, and TEA), whose results demonstrate GRA can improve performance in all cases without adding additional computational cost. © 2022 IEEE.

关键词： Deep learning

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Rfmnet: Robust Deep Functional Maps for Unsupervised Non-Rigid Shape Correspondence

SSRN

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SSRN 2023年

作者： Hu, Ling Li, Qinsong Liu, Shengjun Yan, Dong-Ming Xu, Haojun Liu, Xinru School of Mathematics and Statistics Hunan First Normal University China Institute of Engineering Modeling and Scientific Computing Central South University China Big Data Institute Central South University China National Laboratory of Pattern Recognition Chinese Academy of Sciences China

We introduce a novel strategy to compute the functional map in the deep functional map framework, which jointly considers training stability and more geometric shape features than previous works. We directly first produce a pointwise map by resorting to optimal transport and then convert it to an initial functional map. Such a mechanism mitigates the requirements for the descriptor and avoids the training instabilities resulting from the least square solver. Then, we successfully integrate a state-of-the-art geometric regularization for further optimizing the functional map. We show our novel computing functional map module brings more stable training even under encoding the functional map with high-frequency information and faster convergence speed. Finally, an unsupervised loss is presented for penalizing the correspondence distortion of Delta functions between shapes. Experimental results demonstrate our apparent superiority in correspondence quality and generalization across various shape discretizations and different datasets compared to the state-of-the-art learning methods. © 2023, The Authors. All rights reserved.

关键词： Delta functions

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Prediction of Coagulant Dosage in Waterworks Based on Ensemble Learning

Prediction of Coagulant Dosage in Waterworks Based on Ensemb...

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Information and Communication Technology (ICTech), International Conference of

作者： Yingtao Hu Yonghong Zhang Duan Jian Jin Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Jinan China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing China

There are many processes of water treatment in waterworks, among which coagulation and precipitation plays an important role, which is directly related to the guarantee of water quality. The traditional coagulant dosing technology relies too much on manual labor, has poor stability and high drug consumption. The rapid development of artificial intelligence and machine learning provides a new way to solve the coagulant prediction problem. Combined with the advanced theory of artificial intelligence, a prediction method of coagulant based on multi-model fusion Stacking ensemble learning approach is proposed in this paper. Considering the differences in data observation and training principles of different algorithms, the advantages of each model are fully utilized to build a Stacking ensemble learning model for coagulant prediction with multiple machine learning algorithms embedded, and the base learners of the model contain XGBoost and SVR. data processing goes through steps such as missing value filling, outlier detection, data conversion, and combining with business scenarios for feature construction, feature selection, etc. Finally, the validity of the algorithm was verified using the production data of a water plant. The prediction results show that the coagulant prediction method based on Stacking ensemble learning approach with multi-model fusion has higher prediction accuracy compared with the traditional single-model prediction.

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

arXiv

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arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation

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Comparison of Various Colorization Techniques for MRI Brain Tumor Segmentation using Convolutional Neural Networks

Comparison of Various Colorization Techniques for MRI Brain ...

引用

Recent Advances and Innovations in Engineering (ICRAIE)

作者： Akmal Shafiq Badarul Azam Abdul Kadir Jumaat Shafaf Ibrahim School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Mukah Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia

In the realm of image processing and computer vision, image segmentation takes on a foundational role, serving as a critical inception task for diverse analyses, especially in the application of brain tumor detection. Given the grayscale nature of MRI brain tumor images, the primary objective of this study was to explore the effects of employing various colorization techniques on the accuracy of MRI brain tumor segmentation. The incorporation of color in images has the added benefit of enhancing edge delineation by approximately 10% compared with the grayscale version. To achieve this, a spectrum of color mappings including red, green, blue, yellow, HSV, pseudo-color, and deoldify was applied and compared with the original grayscale MRI images in terms of tumor segmentation accuracy. This comparative segmentation accuracy was performed using two well-established convolutional neural networks (CNNs), namely U-net and SegNet models. Experimental outcomes highlighted the green color mapping as the optimal selection for achieving effective segmentation of meningioma tumors in MRI brain images, followed by the grayscale, HSV, deoldify, pseudo-color, blue, red and yellow color maps. This conclusion was drawn due to the remarkable performance exhibited by the green color mapping across five evaluation metrics, attaining the highest overall average performance measures spanning Dice (0.8876), Jaccard (0.8897), Matthews correlation coefficient (0.8897), Accuracy (0.9954) and remarkably, the lowest recorded Error score (0.0046).

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SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

arXiv

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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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A Novel Cloud-Native and Multi-Platform Parallelized SBAS-INSAR Algorithm

A Novel Cloud-Native and Multi-Platform Parallelized SBAS-IN...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peichen Yu Chao Wang Yixian Tang Weikang Zhang Haihang You Shaoyang Guan Lichuan Zou Hong Zhang Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China International Research Center of Big Data for Sustainable Development Goals Beijing China University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Facing massive Synthetic Aperture Radar (SAR) data, the challenge of achieving rapid and efficient data processing has garnered attention. Currently, most time-series InSAR processing solutions are designed to operate on a single computing platform, leading to drawbacks such as inflexible deployment and slow data transmission. Leveraging the open-source Kubernetes platform, we have employed cloud-native technology to deploy a cross-platform, multi-level parallelization algorithm across multiple nodes. The algorithm is designed based on modular concept, and its modules can successfully achieve cross-platform deployment by relying on both the Network File System (NFS) protocol and the Common Internet File System (CFIS) protocol. This overcomes the limitations of the previous data file storage system, which solely depended on the NFS protocol, leading to deployment on a single operating system. Deploying the algorithm on different operating systems, our results show that the speeds of Linux platform's algorithm parallel modules were improved 16% and 30%, respectively. The successful cross-platform operation of the algorithm enables the data processing workflow to assimilate the strengths of different operating platforms, enhancing data visualization capabilities and gaining support from diverse platform resources. This introduces a new approach for large-scale time-series InSAR processing.

关键词： Cloud computing Protocols File systems Operating systems Linux Geoscience and remote sensing data visualization

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