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arXiv 2023年

作者： Zhong, Xiaojing Huang, Xinyi Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China SenseTime Research China

Makeup transfer is a process of transferring the makeup style from a reference image to the source image, while preserving the source image’s identity. In addition to achieving fine-grained control over the makeup transfer, incorporating semantic alignment into the transformation is crucial, as the poses of the reference and source images are often inconsistent. We propose a novel Semantic-guided Alignment and Region-Adaptive normalization framework (SARA) to effectively transfer makeup styles under misaligned poses, offering flexible control to meet the demands of real-world applications, such as partial transfer, intensity adjustment, and makeup removal. Specifically, SARA comprises three modules: Firstly, we propose a semantic-guided alignment module to explicitly construct dense correspondence between the reference image and the target semantic map, employing unbalanced optimal transport matching to handle semantic region mismatches. Secondly, a region-adaptive normalization module is responsible for dynamically combining the warped style features with shape-independent style codes obtained by region-wise average pooling, mitigating feature loss during the alignment. Lastly, a makeup fusion module progressively fuses the identity features with the makeup styles to render the final output image. Furthermore, we combine optimal transport with histogram matching to generate the pseudo ground truth, which is used to facilitate the transfer in terms of both spatial alignment and color distribution. Experimental results show that our proposed SARA outperforms existing methods on two public datasets. © 2023, CC BY.

关键词： Semantics

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Wideband High-Selectivity SIW Filtering Antenna with Controllable Radiation Nulls

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2024年第1期23卷

作者： Wang, Zhongbao Liu, Xinhong Liu, Hongmei Fang, Shaojun Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Liaoning Huludao125105 China School of Information Science and Technology Dalian Maritime University Liaoning116026 Dalian China

—A novel wideband high-selectivity substrate-integrated-waveguide (SIW) filtering antenna with controllable radiation nulls is proposed. This antenna is composed of a two-layered SIW resonator, two radiating slots on the top layer, and two coupling slots on the middle layer. A vertical SMA connector is located at the bottom and feeds two SIW cavities simultaneously. By adjusting the parameters of these slots, three radiation nulls will be achieved, and two radiation nulls can be easily controlled. For demonstration, one prototype of the filtering antenna is fabricated and measured. Measurement results show that the fractional bandwidth for |S11| © 2024 SBMO/SBMag.

关键词： Substrate integrated waveguides

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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Frontiers of robotics and Software Engineering (FRSE), International Conference on

作者： Shiming He Tongzhijian Yi Bo Yang Chenxi Guo School of Computer and Communication Engineering the Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science & Technology Changsha China

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency.

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DomainVoyager: Embracing The Unknown Domain by Prompting for Automatic Augmentation

DomainVoyager: Embracing The Unknown Domain by Prompting for...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chuang Liu Haogang Zhu Xiu Su State Key Laboratory of Software Development Environment Lab Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Zhongfa Aviation Institute Beihang University Hangzhou P.R.China Zhongguancun Laboratory Beijing China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

For medical image analysis, domain generalization (DG) faces significant challenges due to variances in data across medical imaging devices. Addressing this, we introduce Prompt Guided Domain Aligning Augmentation (PGDAA), a novel approach that harnesses Large Language Models (LLMs) to iteratively refine data augmentation sequences in DG for medical segmentations. Specifically, by harnessing the LLM’s advanced capabilities in interpreting and responding to specific prompts, PGDAA iteratively voyages the parameter searching space for data augmentation, identifying optimal augmentation sequences. In each searching round, the LLM leverages provided prior data augmentation methods, their parameters, and corresponding evaluation results to acquire a sufficient performance memory bank, thereby proposing more effective augmentation sequences to significantly narrowing inter-domain gaps. Notably, this integration of LLMs in DG represents a pioneering application in the field, which adds minor training parameters and can be easily combined with other DG benchmarks for further improvements. Comprehensive experiments reveal our method outperforms the baseline by 4.47% and 5.08% on Fundus and Prostate datasets, achieving 90.10% and 89.28% accuracy, respectively.

关键词： Training Image segmentation Image analysis Accuracy Large language models Benchmark testing data augmentation

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning 13

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhao, Tingting Li, Zuoyong Quan, Li Zeng, Kun Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer Science and Big Data Fuzhou University Fuzhou350121 China

ISBN: (纸本)9798350319156

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance. © 2023 IEEE.

关键词： Textures

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A Model that Learns Discriminative Features for Automatic Sleep Stage Classification Based on Raw Single-Channel EEG

SSRN

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SSRN 2023年

作者： Heng, Xia Wang, Miao Wang, Zhong-Min Zhang, Jie He, Lang Fan, Lin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

Sleep staging is the basis of sleep quality assessment. In the process of scoring each sleep stage, some automatic sleep staging models often fail to effectively capture the more accurate long-range correlation coupling between the input sleep EEG signals and the output sleep stage, which leads to the extracted features cannot effectively distinguish the different sleep stage. In order to alleviate the long-term time series problem of sleep EEG signals and learn the discriminative features of each sleep stage in sleep EEG signals, we propose an end-to-end automatic sleep stage classification method based on raw single-channel sleep EEG signals, which can improve the attention to the discriminative features of each sleep stage. In the construction of the model, in order to learn the source domain information of the key part of the sleep EEG signal, SE-Block is added to Convolutional Neural Network (CNN) to highlight the time-frequency domain features related to the sleep stage. Meanwhile, the attention mechanism is added to the decoding part of Bidirectional Recurrent Unit (Bi-GRU) to enhance the long-term memory ability of Bi-GRU and learn the conversion rules between sleep stages. To validate the accuracy and stability of the model, the Fpz-Cz channel and Pz-Oz channel EEG signals in the Sleep-EDF sleep dataset are used for 10-fold cross-validation. The classification accuracies obtained are 88.48% and 87.56%, respectively. The results show that our model has stronger ability to extract discriminative features, better representation ability, more stable performance and relatively simple structure. © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

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A Weighted Flat Lattice Transformer-based Knowledge Extraction Architecture for Chinese Named Entity Recognition

A Weighted Flat Lattice Transformer-based Knowledge Extracti...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hengwei Zhang Yuejia Wu Jian-Tao Zhou Inner Mongolia University College of Computer Science Hohhot China Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Named Entity Recognition (NER) is one of the contents of Knowledge Extraction (KE) that transforms data into knowledge representation. However, Chinese NER faces the problem of lacking clear word boundaries that limit the effectiveness of the KE. Although the flat lattice Transformer (FLAT) framework, which converts lattice structure into a flat structure including a set of spans, can effectively improve this problem and obtain advanced results, there still exist the problems of insensitivity to entity importance weights and insufficient feature learning. This paper proposes a weighted flat lattice Transformer architecture for Chinese NER, namely WFLAT. The WFLAT first adds a weight matrix into self-attention calculation, which can obtain finer-grained partitioning of entities to improve experimental performance, and then adopts a multi-layer Transformer encoder with each layer using a multi-head self-attention mechanism. Extensive experimental results on benchmarks demonstrate that our proposed KE model can obtain state-of-the-art performance for the Chinese NER task.

关键词： Representation learning Computational modeling Memory management Lattices Named entity recognition Transforms Knowledge representation

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Local Entropy Based Fuzzy Connectedness Segmentation for Thyroid Ultrasound Images

Local Entropy Based Fuzzy Connectedness Segmentation for Thy...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yixuan Fu Kai Li Junying Chen Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education School of Software Engineering South China University of Technology Guangzhou China Department of Ultrasound Third Affiliated Hospital of Sun Yat-sen University Guangzhou China

Although deep learning methods are popular in recent years to achieve dominant image segmentation performance, the carefully annotated training images are very difficult to obtain in clinical medicine, and the deep learning model training demands a lot of computing resources which are not always available. To overcome these difficulties, it is very necessary to investigate segmentation algorithms which do not require any training data. In this work, we improve a semi-automatic conventional segmentation algorithm, fuzzy connectedness segmentation, by introducing the local information entropy of the ultrasound image into the fuzzy affinity membership function. The proposed semi-automatic local entropy based fuzzy connectedness (LEFC) segmentation algorithm is further modified by incorporating the popular deep learning methods to make the LEFC algorithm fully automatic. The proposed semi-automatic LEFC algorithm achieves better performance than other representative fuzzy connectedness segmentation algorithms with the accuracy and IoU values increased by 5.9~14% and 5~10.5%, and achieves very close segmentation performance as compared with the best deep learning model in the experiments (0.2% lower accuracy and 0.2% lower IoU compared to CE-Net). Besides, the automatic LEFC algorithm improves the segmentation results obtained by the corresponding deep learning based coarse segmentation modules with the accuracy and IoU increased by 0.2~8% and 0.1~3.8%, respectively. Furthermore, the experiment results demonstrate that the proposed automatic LEFC algorithm obtains stably good segmentation performance even with limited training data, achieving the accuracy and IoU values (at the stable state) 23.6 % and 18.5% higher than those of the corresponding U-Net model which is trained with the samesized dataset.

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Vehicle Segmentation with Coarse Distance Estimation Based on Monocular Vision 17

Vehicle Segmentation with Coarse Distance Estimation Based o...

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17th IEEE International Conference on E-Business Engineering (ICEBE)

作者： Xie, Fangyuan Yin, Pengshuai Ke, Zehui Sun, Ruizhou Ding, Ke Chen, Jian Wu, Qingyao South China Univ Technol Sch Software Engn Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9781665444187

With the increasing population of drivers and the popularity of intelligent cars, traffic safety is becoming more concerned. Aiming to reduce the probability of road accidents, many works have explored how to provide an early warning when the distances between the current car (or the in-car camera) and vehicles ahead are dangerous. In this paper, benefiting from the robust pixel-level understanding of cutting-edge deep networks, we introduce a vehicle segmentation method based on monocular vision, detecting vehicles in front of the current one and generating a segmentation mask for each vehicle instance detected. Instead of concentrating our efforts on measuring distance precisely, we categorize distances into three classes ("near", "medium", and "far"), which are pretty enough for an early collision warning. Our model is trained and evaluated on our hand-made dataset, after which we transfer and deploy it onto an Android TV virtual machine to test its performance. All the results we got show that our method does a good job in vehicle segmentation with coarse distance estimation, being applicable to aided driving.

关键词： vehicle segmentation distance estimation monocular vision

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Automatic Deep Active Contour Model Based on Local Image Characteristics

Automatic Deep Active Contour Model Based on Local Image Cha...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haijun You Kai Li Junying Chen Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education School of Software Engineering South China University of Technology Guangzhou China Department of Ultrasound Third Affiliated Hospital of Sun Yat-sen University Guangzhou China

In recent years, convolutional neural networks have made remarkable achievements in medical image segmentation. Unclear margins and fuzzy boundary regions are formed surrounding the thyroid gland area under the influence of artifacts and speckles, which increases the difficulty of thyroid ultrasound image segmentation. However, building an effective CNN model usually requires massive computing resources, long training time, and large amounts of carefully labeled data. In this work, we develop a fast, compact, accurate and robust image segmentation model for automatic thyroid segmentation in medical ultrasound images. We propose an automatic deep active contour (DAC) model based on local image characteristics, which utilizes the deep neural network model to obtain the initial contour and constructs external energy items based on the local image characteristics of contour points. Compared with the deep neural network model, the DAC model requires less training data, and the segmentation performance of the DAC model on a small dataset can reach that of the deep neural network model on a larger dataset. The proposed DAC model can be used for automatic thyroid segmentation in medical ultrasound images, which does not require massive computing resources, long training time, and large amounts of carefully labeled data.

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