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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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Multi-Pyramid Dehazing Network with Residue Channel Enhancement 2

Multi-Pyramid Dehazing Network with Residue Channel Enhancem...

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2022 2nd International Conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2022

作者： Hang, Ruishan Gu, Shenming Tang, Zhibo Tan, Xiaoqiu School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9783800760268

Single image dehazing is to remove haze from degraded images and recover clean scenes. The researches of dehazing have academic significance and application value. In this paper, we design a Multi-Pyramid Dehazing Network (MPDN) with Residue Channel Enhancement (RCE). A RCE module was designed to help the network model remove a large amount of homogeneous haze. To further improve the effect of eliminating heterogeneous haze, the Pyramid Feature Extraction (PFE) module is used. The two modules complement each other to achieve excellent haze removal effect. Finally, we do a lot of experiments to verify the model, and compare it with the state-of-the-art (SOTA) method on the benchmark datasets. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Demulsification

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Overview of indoor positioning technology 24

Overview of indoor positioning technology

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2024 International Conference on Image Processing, Intelligent Control and Computer engineering, IPICE 2024

作者： Xu, Zeyu Huang, Fenghua The Academy of Digital China Fuzhou University China College of Artificial Intelligence Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China

ISBN: (纸本)9798400710285

This paper introduces the classification of indoor positioning technology, and elaborates the principles of three mainstream indoor positioning technologies based on external source, natural source and multi-source fusion and their development respectively. By analyzing the problems of existing indoor positioning technologies, it summarizes the difficulties and future trends of the current development of indoor positioning technologies at home and abroad. The comprehensive study shows that exploring combined positioning techniques and better positioning algorithms, and combining them with machine learning (including deep learning) to further improve indoor positioning accuracy, establishing a standard system and performance evaluation system for indoor positioning techniques, and focusing on user privacy are the main future development directions of indoor positioning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Review and prospect of remote sensing monitoring methods for inland black and malodorous water bodies 24

Review and prospect of remote sensing monitoring methods for...

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2024 International Conference on Image Processing, Intelligent Control and Computer engineering, IPICE 2024

作者： Liao, Yansong Huang, Fenghua The Academy of Digital China Fuzhou University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China College of Artificial Intelligence Yango University China

ISBN: (纸本)9798400710285

Black and malodorous water bodies represent a severe water pollution phenomenon that urgently needs addressing due to the discomfort and health risks they pose. Since the release of the"Action Plan for Prevention and Control of Water Pollution" in 2015 and the subsequent"Action Plan for the Treatment of Black and Malodorous Water Bodies in Urban Areas during the 14th Five-Year Plan Period" in 2022 by the Chinese government, monitoring efforts have become increasingly crucial. Remote sensing technology offers advantages such as high dynamics, low cost, and a macroscopic perspective in ecological environment monitoring, making it advantageous over traditional physical and chemical methods in monitoring black and malodorous water bodies. This paper summarizes remote sensing methods for identifying black and malodorous water bodies, including extraction of information through spectral, spatial, index, and temporal features, and analyzes the monitoring feasibility of these methods while pointing out potential issues. Finally, future research directions for identifying black and malodorous water bodies are discussed. © 2024 Copyright held by the owner/author(s).

关键词： Environmental monitoring

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Image recognition and empirical application of desert plant species based on convolutional neural network

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Journal of Arid Land 2022年第12期14卷 1440-1455页

作者： LI Jicai SUN Shiding JIANG Haoran TIAN Yingjie XU Xiaoliang School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China

In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching *** plant taxonomic identification requires high expertise,which is *** nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status *** and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network *** 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep *** desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in *** results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,*** t

关键词： desert plants image recognition deep learning convolutional neural network Residual Network X_8GF(RegNetX_8GF) Mobile Network V2(MobileNetV2) nature reserves

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Intelligent Aquaculture Water Quality Detection Scheme Based on TurMass Technology 8

Intelligent Aquaculture Water Quality Detection Scheme Based...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhang, Yafang Huang, Yongxin Chen, Hongming Zhejiang Ocean University School of Information Engineering Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Nankai University College of Electronics Information and Optical Engineering Tianjin300350 China

ISBN: (纸本)9798350302455

To address the shortcomings of wireless data transmission in aquaculture water quality monitoring, we have developed an intelligent water quality detection device based on the STM32 microcontroller system as the processing unit and TurMass transmission technology. This device can detect water quality in real-time and transmit the data to the user end for real-time monitoring so that users can monitor water quality in real-time. Through rigorous and systematic experimental measurements, the accuracy of the detected data by the device was found to be extremely high, indicating that it can be used in practical applications. © 2023 IEEE.

关键词： Water quality

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A Brief Survey of Distribution Robust Graph Neural Networks 11

A Brief Survey of Distribution Robust Graph Neural Networks

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

作者： Zheng, Lei Quan, Pei Shi, Yong Niu, Lingfeng The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China The College of Economics and Management Beijing University of Technology Beijing100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing1001090 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction. © 2024 The Authors.

关键词： Distribution generalization Graph neural networks Shift robust

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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

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IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography data mining Mental disorders Multisensory integration

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MFENet: Multi-Feature Extraction Net for Remote Sensing Semantic Segmentation 7

MFENet: Multi-Feature Extraction Net for Remote Sensing Sema...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Zhang, Chao Lu, Xingshun Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

In this paper, we tackle the remote sensing semantic segmentation task by capturing feature information across multiple scales, all channels, and global locations. Different from previous works that simply use U-net to extract multi-scale features, we further improve U-net and propose a Multi-Feature Extraction Network (MFE-Unet). Specifically, we propose the MFE module, which uses both dilated convolution module and two attention modules. Dilated convolution is used to enhance U-net's ability to represent multi-scale information. The two attention modules refer to the channel attention module and the pixel attention module. Channel attention maps all channels centrally, assigns weights uniformly, and adaptively adjusts the importance of each channel's information. Pixel attention treats features at each location as the same individual, and similar features will be associated together to further improve feature representation. We conducted multiple sets of experiments on the "AI+"remote sensing image dataset. Experiments show that our network is sufficient against several advanced models. © 2022 IEEE.

关键词： Convolution

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