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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Learning a Layout Transfer Network for Context Aware Object Detection

arXiv

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arXiv 2019年

作者： Wang, Tao He, Xuming Cai, Yuanzheng Xiao, Guobao Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Science and Technology ShanghaiTech University Shanghai China College of Mathematics and Computer Science Fuzhou University NetDragon Fuzhou China

We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network [1] to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN [2] to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-of-the-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions

arXiv

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arXiv 2024年

作者： Huang, Wenfeng Xu, Guoan Jia, Wenjing Perry, Stuart Gao, Guangwei The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Images captured in challenging environments–such as nighttime, foggy, rainy weather, and underwater–often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed "ReviveDiff", which can address a wide range of degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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Escalator passenger posture anomaly detection based on improved SSD model

Escalator passenger posture anomaly detection based on impro...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Teng Xu Jian Chen Zuoyong Li Yuanzheng Cai School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation.

关键词： Euclidean distance Market research Inference algorithms Real-time systems Safety Public healthcare information technology

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Tourist Attraction Recommendation System Based on Django and Collaborative Filtering 1st

Tourist Attraction Recommendation System Based on Django and...

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Jiang, Yufeng Zhang, Yushu Li, Zhujun Yu, Wendong Wei, Hongwei Yuan, Lin Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China School of Art Nantong University Jiangsu Nantong226019 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China Shandong Tianyi Information Technology Co. Ltd. Jinan Shandong250101 China

ISBN: (纸本)9789819708260

Recently, with the development of social and economic levels and people's pursuit of quality of life, tourism has become the first choice for more and more people. However, the traditional travel agency-based tourism method has also begun to expose some problems. However, with the development of machine learning and big data technology, personalized recommendation systems have become more and more important, which also provides us with new ideas to solve this problem. This paper therefore explores the application of spatial clustering algorithm and collaborative filtering algorithm in tourist attraction recommendation. This algorithm can not only mine the geographical location information of tourist attractions, but also analyze and study the data connections between users and attractions, and use these data to make personalized recommendations, providing more considerate and convenient services for travel planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering

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A New and Efficient Dormitory Management System 1st

A New and Efficient Dormitory Management System

引用

1st International Conference on Applied Intelligence, ICAI 2023

作者： Meng, Boyuan Zhang, Yushu Li, Zhujun Yu, Wendong Wei, Hongwei Yuan, Lin Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China School of Art Nantong University Jiangsu Nantong226019 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China

ISBN: (纸本)9789819708260

Today’s information trend is unstoppable, and the manual management mode of college dormitory management cannot meet the trend of increasing demand for dormitories year by year. It not only wastes many manpower and material resources, but also is easy to appear many unnecessary low-level errors. A system about student dormitory management not only saves many unnecessary repeated working time, but also reduces the generation of various low - level errors in information management, which makes the grasp and operation of information more convenient. The system adopts B/S architecture (browser / server architecture), mainly based on the development and implementation of JAVA language. The system has 3 user roles: administrator, student, and host administrator. The functional module of the administrator includes user management, student management, dormitory management, grade management and authority management, which can realize the management of dormitory information and other information. Students’ function module includes student personal details management, online pre-selection dormitory and view of absence records, students can pre-select the dormitory, and view personal information and absence information. The dormitory manager function module includes dormitory management, pre-selection setting and absence management, dormitory details management and absence management, and pre-selection time settings. After testing, the college dormitory management system can effectively realize the above functions and is interface friendly and easy to operate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Fall Detection Based on Person Detection and Multi-target Tracking

Fall Detection Based on Person Detection and Multi-target Tr...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

ISBN: (纸本)9781728194202

Recently, official statistics reported that the Chinese population aged 60 and above has been 26.402 million, which accounts for 18.70% of total population. It is urgent to develop fall detection technologies for alleviating the risk causing by falling of elder person. In this paper, we propose a real-time, high-precision, and deep learning-based fall detection method with automatic person detection and tracking. Specifically, the proposed method first improves the YOLOv3 network to more efficiently detect person and extract feature maps of the object. Then, it inputs the extracted feature maps from the YOLOv3 into a multi-target tracking network for cascade matching and IOU matching in a Deep SORT algorithm, respectively. Next, it improves YOLOv5 network to detect posture anomalies. Finally, it refines the detected posture anomalies for obtaining the final fall detection result. Experimental results show that the proposed method simultaneously improves accuracy and efficiency of the fall detection.

关键词： Deep learning Sociology Education Object detection Aging Feature extraction Real-time systems

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Overlapping Community Detection Based on Influence and Seeds Extension

引用

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2019年第1期47卷 153-160页

作者： Yu, Zhi-Yong Chen, Ji-Jie Guo, Kun Chen, Yu-Zhong Xu, Qian College of Mathematics and Computer Sciences Fuzhou University FuzhouFujian350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing FuzhouFujian350116 China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing FuzhouFujian350116 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. FuzhouFujian350003 China

Community detection is a significant research direction in the research of social networks. To improve the quality of seeds selection and expansion, we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection. First, i-SEOCD uses a node influence strategy to find the seed communities with tight structures. Second, on the basis of the seed communities, we calculate the similarity among communities and their neighbor nodes. The nodes whose similarity is greater than a predefined threshold are selected. Third, the strategy of optimizing a self-adaptive function is adopted to expand the communities. Finally, the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures. Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently. © 2019, Chinese Institute of Electronics. All right reserved.

关键词： Population dynamics

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Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset

arXiv

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arXiv 2023年

作者： Liu, Lei Zhang, Mengya Li, Cheng Li, Chenglong Tang, Jin The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China The Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing The Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China The School of Computer Science and Technology Anhui University Hefei230601 China

Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multi-modal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called Modality-Aware Fusion Network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module. The adaptive weighting module predicts fusion weights to dynamically adjust the contribution of each modality, while the modality-specific representation module captures discriminative features specific to RGB and NIR modalities. MAFNet offers great flexibility as it can effortlessly integrate into diverse tracking frameworks. With its simplicity, effectiveness, and efficiency, MAFNet outperforms state-of-the-art methods in cross-modal object tracking. To validate the effectiveness of our algorithm and overcome the scarcity of data in this field, we introduce CMOTB, a comprehensive and extensive benchmark dataset for cross-modal object tracking. CMOTB consists of 61 categories and 1000 video sequences, comprising a total of over 799K frames. We believe that our proposed method and dataset offer a strong foundation for advancing cross-modal object tracking research. The dataset, toolkit, a

关键词： Large dataset

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Triangle-oriented Community Detection considering Node Features and Network Topology

arXiv

引用

arXiv 2022年

作者： Gao, Guangliang Liang, Weichao Yuan, Ming Qian, Hanwei Wang, Qun Cao, Jie Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Network topology

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