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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qian Huang Wenting Liu Xin Li Yiming Wang College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR.

关键词： Interpolation Adaptive systems Costs Acoustic distortion Accuracy Motion estimation Video compression Feature extraction Motion compensation Speech processing

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Metric localization for lunar rovers via cross-view image matching

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Visual Intelligence 2024年第1期2卷 1-14页

作者： Chen, Zihao Li, Kunhong Li, Haoran Fu, Zhiheng Zhang, Hanmo Guo, Yulan School of Electronics and Comunication Engineering the Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen 510275 China Peng Cheng Laboratory Guangdong Shenzhen 518055 China Department of Computer Science and Software Engineering The University of Western Australia Perth 6009 Australia Department of Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology Institute Shanghai 201109 China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha 410073 China

Accurate localization is critical for lunar rovers exploring lunar terrain features. Traditionally, lunar rover localization relies on sensor data from odometers, inertial measurement units and stereo cameras. However, localization errors accumulate over long traverses, limiting the rover’s localization accuracy. This paper presents a metric localization framework based on cross-view images (ground view from a rover and air view from an orbiter) to eliminate accumulated localization errors. First, we employ perspective projection to reduce the geometric differences in cross-view images. Then, we propose an image-based metric localization network to extract image features and generate a location heatmap. This heatmap serves as the basis for accurate estimation of query locations. We also create the first large-area lunar cross-view image (Lunar-CV) dataset to evaluate the localization performance. This dataset consists of 30 digital orthophoto maps (DOMs) with a resolution of 7 m/pix, collected by the Chang’e-2 lunar orbiter, along with 8100 simulated rover panoramas. Experimental results on the Lunar-CV dataset demonstrate the superior performance of our proposed framework. Compared to the second best method, our method significantly reduces the average localization error by 26% and the median localization error by 22%. © The Author(s) 2024.

关键词： Aerial patch Cross-view metric localization (CVML) Lunar rover Panorama Perspective projection

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NucleiFormer: A Nuclei Segmentation Model Optimized by Joint Haar Wavelet and Adaptive Feature Calibration

NucleiFormer: A Nuclei Segmentation Model Optimized by Joint...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yulin Chen Qian Huang Zhijian Wang Ziyang Yin Meng Geng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nucleus segmentation plays a vital role in medical image analysis. However, existing segmentation methods frequently encounter hurdles, such as the loss of crucial image details during downsampling and issues like noise and spatial displacement. In this study, we propose NucleiFormer, where Haar wavelet transforms are employed in the encoder to replace conventional downsampling techniques. Additionally, we utilize an adaptive feature calibration module to align and calibrate features of different scales, reduce feature redundancy, suppress noise, and enhance the spatial awareness of the model. Extensive experiments validate our model’s superior segmentation performance in accurately delineating nucleus boundaries and minimizing errors.

关键词： Wavelet transforms Adaptation models Image segmentation Image analysis Noise Termination of employment Calibration Speech processing Optimization Biomedical imaging

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Spatial Consistency and Feature Diversity Regularization in Transfer Learning for Fine-Grained Visual Categorization

Spatial Consistency and Feature Diversity Regularization in ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Dai, Zhigang Chen, Junying Mian, Ajmal South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education School of Software Engineering Guangzhou China The University of Western Australia Department of Computer Science Perth Australia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Fine-grained visual categorization is challenged by limited training data by localizing discriminative regions and learning diverse features. We propose an effective regularization method that simultaneously imposes spatial consistency and feature diversity on CNN feature maps from a unified perspective. The former guides different feature map channels to concentrate collaboratively on the discriminative areas while the latter ensures that the feature maps are diverse. The proposed method does not require additional supervision, and leverages the covariance matrix of multi-channel feature maps to regularize the loss at the last convolutional layer where the semantic information is the richest. This allows the influence to be backpropagated to update all convolutional layers. We perform experiments using four network architectures for transfer learning from two source domains to three target domains, and demonstrate that our regularization method improves accuracy in all different settings. The proposed regularization method achieves state-of-the-art performance on CUB-200-2011, Stanford-Cars, and Stanford-Dogs datasets with 89.8%, 94.6%, and 88.5% accuracy, respectively. © 2022 IEEE.

关键词： Covariance matrix

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Seed Feature Maps-based CNN Models for LEO Satellite Remote ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Lu, Zhichao Ding, Chuntao Wang, Shangguang Cheng, Ran Juefei-Xu, Felix Boddeti, Vishnu Naresh Sun Yat-sen University School of Software Engineering Zhuhai China Beijing Jiaotong University School of Computer and Information Technology Beijing China Beijing University of Posts and Telecommunications Key Laboratory of Networking and Switching Technology Beijing China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China New York University New YorkNY United States Michigan State University Department of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350304855

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3 × fewer parameters and 2.2 × fewer FLOPs. © 2023 IEEE.

关键词： Neural network models

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Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information 8

Unsupervised Semantic Representation Learning of Scientific ...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Gao, Hongrui Li, Yawen Liang, Meiyu Guan, Zeli Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Economics and Management Beijing100876 China

ISBN: (纸本)9781665477352

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features makes the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning. © 2022 IEEE.

关键词： Graph neural networks

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UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Network for Short Text Classification 15

UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Net...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Kong, DeYan Ji, ZhenYan Sang, Yanjuan Dong, Wei Yang, Yanyan Beijing Jiaotong University School of Software Engineering Beijing China Blueintelligence Technology Co.Ltd. R&d Department Wuhan China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing100190 China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

During the last decade, deep learning has played a significant role in improving text classification which is widely applied in question answering systems, comment extraction, sentiment analysis, etc. Considering that few labeled training data are available, semi-supervised short text classification needs to be studied urgently. Existing works focus on long texts and cannot achieve satisfactory performance on short texts due to the sparsity, non-standardization, and insufficient labeled data. In this paper, we propose a novel uncertainty-aware heterogeneous graph attention network for semi-supervised short text classification (UA-HGAT), which makes full use of limited labeled data and large amounts of unlabeled data via exploiting an uncertainty-aware mechanism. In the model, entity description from Wikipedia is introduced to empower short texts, and word-level information is acquired by Word2Vec. The entity description and word-level information are combined with topic information to address the issue of short text sparseness. In particular, we introduce uncertainty-aware mechanism and negative sample learning to relieve the erroneous high confidence predictions from poorly calibrated models, so that it can offer enough pseudo-label samples for the model training. Extensive experiments show that our model significantly outperforms the SOTA methods on AGNews and Snippets datasets. © 2022 IEEE.

关键词： Sentiment analysis

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Expression Recognition and Intelligent Classroom State Mining in Teaching Videos Based on Semi-Supervised Learning Generative Adversarial Network

Expression Recognition and Intelligent Classroom State Minin...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Ruoyu Fan Meiyu Liang Mengran Yin Junping Du Beijing Key Laboratory of Intelligent Communication Software Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset.

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An Experimental Study of Unsupervised Rank Aggregation Methods in World University Rankings

An Experimental Study of Unsupervised Rank Aggregation Metho...

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Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Shiwei Feng Qi Deng Siyi Wang Lin Song Chao Liang Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software (NERCMS) School of Computer Science Wuhan University

Recently, more and more college ranking systems are receiving attention due to the demand and necessity of higher education and college ranking system is a key topic in the field of social choice. However, these rankings have different evaluation criteria that lead to confusion for decision-makers. To address this issue, a simple and practical approach is to aggregate these ranking systems from different sources. In this paper, we conduct an experimental study on aggregation of world university ranking. Specifically, we first classify unsupervised RA methods. Then, we compare the aggregation effects of 28 unsupervised RA methods on five public university rankings.

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