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Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking 12th

Temporal Global Re-detection Based on Interaction-Fusion At...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ma, Jingyuan Hou, Zhiqiang Han, Ruoxue Ma, Sugang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China

ISBN: (纸本)9783031463075

In long-term visual tracking, target occlusion and out-of-view are common problems that lead to target drift, adding re-detection to short-term tracking algorithms is a general solution. To better handle the problem of target disappearing and reappearing in long-term visual tracking, this paper proposes a temporal global re-detection method based on interaction-fusion attention. Firstly, ResNet50 is used as the feature extraction network to obtain the depth features of the template and the search region. Then, a new interaction-fusion attention is added to extract the connection of different dimensionality of features. Finally, Temporal ROI Align is introduced to select candidate boxes, increasing the use of historical information by re-detection method, and improving the accuracy of target localization. STMTrack algorithm is selected as the short-term tracking algorithm, which works with the proposed re-detection method to construct a long-term tracking algorithm, and experiments are conducted on the UAV123, LaSOT, UAV20L, and VOT2018-LT datasets, and the effectiveness of this re-detection method can be seen from the experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical detection

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

Suppress Content Shift: Better Diffusion Features via Off-th...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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A Unified Framework Integrating Knowledge and data for Collaborative Root Cause Identification

A Unified Framework Integrating Knowledge and Data for Colla...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Jiefei Yu Zicheng Cao Siyi He Zuyi Gu Yingcheng Xu Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Anhui China Data Science and Big Data Technology School of Big Data and Statistics Anhui University Anhui China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller.

关键词： Fault diagnosis Knowledge engineering Reactive power Analytical models Delay effects Redundancy Collaboration Cause effect analysis Vectors Safety

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Helmet Detection Algorithm of Electric Bicycle Riders Based on YOLOv5 with CBAM Attention Mechanism Integration 8th

Helmet Detection Algorithm of Electric Bicycle Riders Based ...

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8th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2023

作者： Fu, Si-Yue Wei, Dong Zhou, Liu-Ying School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Beijing100044 China

ISBN: (纸本)9789819975921

Object detection algorithms can assist in detecting the helmet-wearing status of electric bicycle riders, thereby saving regulatory manpower costs. However, there is currently a lack of standardized and publicly available datasets. Additionally, the basic YOLOv5s object detection algorithm, due to its limited feature extraction capabilities, may lead to numerous instances of both false negative and false positive. To enhance the model’s focus on critical information within the feature maps, this paper introduces the CBAM attention mechanism module into the Backbone section of YOLOv5s. This module sequentially infers attention maps from the input feature maps along both channel and spatial dimensions independently, and then multiplies these attention maps with the input feature maps to achieve adaptive feature optimization. This paper have established self-built dataset for experimental research, and the results indicate that compared to the original YOLOv5s model, the proposed method has improved the model’s overall mAP score by 1.89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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An Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation 3

An Enhanced-Precision Bandgap Reference Incorporating Temper...

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3rd International Symposium on Semiconductor and Electronic Technology, ISSET 2024

作者： Xiang, Tianzi Xu, Quanyuan Liu, Haorui Lu, Ying Southwest Forestry University College of Big Data and Intelligent Engineering Yunnan Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Yunnan Kunming China Changchun University of Science and Technology School of Physics Jilin Changchun China

ISBN: (纸本)9798350390285

In this work, an Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation was proposed. Unlike traditional bandgap references, the reference voltage output of 0.701 V is realized by combining reference current and resistor. Meanwhile, an innovative Linear-piecewise current compensation was proposed, which aimed at optimizing the temperature drift of the bandgap reference. The simulation results shows that the proposed curvature-compensation technique improves the TC from 24.96 to 2.14 ppm°C in the temperature range of -40 °C to 120 °C;the PSRR of Enhanced-Precision Bandgap Reference at low frequency is 72.40 dB;the line regulation is about 0.08 %/V for input voltages between 3.2 and 3.7 V in standard 0.18 μm 1P6M CMOS process. The proposal of this new circuit provides a new idea for the realization of high- performance integrated circuits in the future. © 2024 IEEE.

关键词： Piecewise linear techniques

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Tri-Training with Granular-Ball in Weighted Fuzzy Rough Sets 11th

Tri-Training with Granular-Ball in Weighted Fuzzy Rough Se...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xu, Gaojie Lian, Xiaoyu Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9783031927461

This paper proposes a Tri-Training framework based on the Weighted Fuzzy Rough Sets and integrates the Granular-Ball method to enhance semi-supervised learning through multi-view granulation. Traditional Tri-Training methods, while effective in leveraging unlabeled data, often suffer from label propagation inaccuracies. Moreover, despite their strengths in handling high-dimensional and uncertain data, Weighted Fuzzy Rough Sets face challenges in robustness and feature selection. To address these limitations, we introduce Granular-Ball computing to generate multi-level granular representations, promoting classifier diversity and reducing redundancy. Additionally, we employ the Weighted Fuzzy Lower Approximation classifier to improve robustness in uncertain environments. Experimental results on multiple benchmark datasets demonstrate that our approach consistently outperforms traditional semi-supervised learning methods, particularly in handling noisy data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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A Privacy Leak Detection Mechanism based on Service Binding 10

A Privacy Leak Detection Mechanism based on Service Binding

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Wang, Boyang He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid. © 2024 IEEE.

关键词： Static analysis

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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GraphConvNet: A Dual Network Utilizing Local Features Coupled with Structural Information for Predicting Knee Osteoarthritis

GraphConvNet: A Dual Network Utilizing Local Features Couple...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pengju Tang Dawei Dai Kai Zou Xionghui Yang Guoyin Wang Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor’s clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet.

关键词： Representation learning Visualization Decision making Medical services Feature extraction Graph neural networks Convolutional neural networks Osteoarthritis Diagnostic radiography Diseases

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