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Deep Learning-based Image Text Processing Research*

Deep Learning-based Image Text Processing Research*

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IEEE International Conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart Computing (HPSC) and Intelligent data and Security (IDS)

作者： Huixuan Xiong Kai Jin Jingnian Liu Jiahong Cai Lijun Xiao School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning Image forensics Recurrent neural networks Computational modeling Text categorization Feature extraction data models

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Viewed Lightness Prediction Model of Textile Compound Multifilament 11th

Viewed Lightness Prediction Model of Textile Compound Multif...

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11th China Academic Conference on Printing and Packaging, CACPP 2020

作者： Wang, Yujuan Wang, Jun Yuan, Jiangping Tian, Jieni Chen, Guangxue State Key Laboratory of Pulp and Paper Engineering South China University of Technology Guangzhou China College of Textiles Donghua University Shanghai China Institute for Visualization and Data Analysis Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9789811605024

To facilitate the design of the textile compound multifilament before spinning, a viewed lightness prediction model was proposed. To examine the prediction effect, 7 different color monofilaments were used to simulate 21 multifilaments according to a simplified multifilament model. Then, the mixed lightness of these multifilaments were calculated by adding the weighted tristimulus values of the surface color of a multifilament, and scaled by 12 observers in the subjective experiment. The average difference between the calculated and scaled lightness was 0.39. In order to determine the significant factors affecting the lightness difference, the Pearson’s correlation analysis was conducted. The results show that the lightness difference decreased with the increase of the calculated CIE lightness (L*) or yellow-blue value (b*) of the multifilament. Finally, the optimized viewed lightness prediction model was derived by the SPSS analysis. The average lightness difference was reduced to -0.01, indicating this model can provide reliable prediction for personalized compound multifilament. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Collaborative Optimization-Guided Entity Extraction Scheme 17th

A Collaborative Optimization-Guided Entity Extraction Scheme

引用

17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Peng, Qiaojuan Luo, Xiong Shen, Hailun Huang, Ziyang Chen, Maojian School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Foshan528399 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Ouyeel Co. Ltd. Shanghai201999 China

ISBN: (纸本)9783030926373

Entity extraction as one of the most basic tasks in achieving information extraction and retrieval, has always been an important research area in natural language processing. Considering that most of the traditional entity extraction methods need to manually adjust their hyperparameters, it takes a lot of time and is easy to fall into local optimality. To avoid such limitations, this paper proposes a novel scheme to extract named entities, where the model hyperparameters are automatically adjusted to improve the performance of entity extraction. Here, the proposed scheme is composed of bi-directional encoder representation from transformers (BERT) and conditional random field (CRF). Specifically, through the fusion of collaborative computing paradigm, particle swarm optimization (PSO) algorithm is utilized in this paper to search for the best value of hyperparameters automatically in a cooperative way. The experimental results on two public datasets and a steel inquiry dataset verify that our proposed scheme can effectively improve the performance of entity extraction. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Particle swarm optimization (PSO)

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Event-driven Robotic Tactile data Learning Using Temporal Spike Sequence Backpropagation Method

Event-driven Robotic Tactile Data Learning Using Temporal Sp...

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Chinese Automation Congress (CAC)

作者： Qing Hou Tingqing Liu Jing Yang Xiaoyang Ji Qinglang Li Jian Li Baofan Yin School of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University School of Mechanical Engineering Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China

Tactile perception is indispensable for intelligent robots to interact intelligently like humans. Therefore, the effective use of deep learning methods to acquire tactile features has become an important focus of tactile perception research. Satisfactory time-driven characteristics and the ability to process spatiotemporal information efficiently of spiking neural networks are advantageous for event-based data. We apply a temporal spike sequence learning backpropagation method that can handle continuous spikes to improve the spike neural network for tactile object recognition based on event-driven data. We prove the effectiveness of the temporal spike sequence error backpropagation method in practical applications to address the problem of losing temporal information of data using approximate derivatives. In practical application, we have proved the validity of the back propagation method of temporal spike sequence error in solving the problem of losing temporal information of data using approximate derivatives

关键词： Backpropagation Deep learning Automation Neural networks data models Spatiotemporal phenomena Object recognition

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3D Human Body Reconstruction from a Single Image 15th

3D Human Body Reconstruction from a Single Image

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15th Conference on Image and Graphics Technology and Applications, IGTA 2020

作者： Xu, WenTao Wu, ChunHong Li, Tian Fu, Dongmei Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing Fo Shan528300 China

ISBN: (纸本)9789813360327

In this paper, a generative adversarial network based on SMPL model is used to reconstruct three-dimensional (3D) human mesh from a single image. The generative adversarial network consists of two parts (generative network and adversarial network). The generative network is made up of a deep residual network and a multilayer perception network. The goal is to generate suitable SMPL model parameters and camera parameters which fit the 2D and 3D position annotation of human skeleton joints. The adversarial network judges whether the shape and pose parameters generated by the generative network match the real human body through a discriminator. The purpose is to guide the generative network output parameters that conform to the laws of human kinematics. A geometric prior of the 3D joint angle limitation of the human body is introduced into the network to further constrain the generated human pose. Experiments verify the effectiveness of the algorithm. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Fast parallel extraction method of normalized vegetation index 15

Fast parallel extraction method of normalized vegetation ind...

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15th International Conference on Computer Science and Education, ICCSE 2020

作者： Zuo, Xianyu Qi, Tongyuan Qiao, Baojun Deng, Zhitao Ge, Qiang Henan University Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781728172675

At present, accelerated processing of remote sensing big data has become an important research topic in the field of remote sensing. Remote sensing image processing based on large-scale clusters currently is the mainstream. However, how to fully tap the computing power of a single computing node in the cluster has become issues that cannot be ignored in the field of remote sensing image processing. Traditional GPU programming is difficult to develop, the development cycle is long, and the requirements for developers are very high. In order to improve the efficiency of GPU programming and shorten the development cycle of parallel programs, Nvidia, Grary, PGI and CAPS jointly launched a new programming standard-OpenAcc. In this paper, OpenAcc-NDVI, as a fast parallel extraction method is used to optimize NDVI algorithm. Based on different computing scenarios, two granularity acceleration models are proposed. It has been verified by multiple experiments that when the data size reaches 10000 ∗ 10000, OpenAcc-NDVI can achieve an acceleration of about 5.3 times. After error analysis, the error of algorithm experiment result is 0, there is no loss of precision. The NDVI algorithm based on OpenAcc has excellent acceleration performance, efficient development process, and high calculation accuracy. © 2020 IEEE.

关键词： Graphics processing unit

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Thinking in Patch: Towards Generalizable Forgery Detection with Patch Transformation 18th

Thinking in Patch: Towards Generalizable Forgery Detection w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zhang, Xueqi Wang, Shuo Liu, Chenyu Zhang, Min Liu, Xiaohan Xie, Haiyong University of Science and Technology of China Hefei230026 China Key Laboratory of Cyberculture Content Cognition and Detection Ministry of Culture and Tourism Anhui230027 China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Beijing100040 China

ISBN: (纸本)9783030893699

Nowadays, synthetic faces can completely trick human eyes, which raises social concerns for malicious dissemination of such fake content. As a result, face forgery detection has become a significant research topic. Due to the different distributions of synthetic data in different generation algorithms, it is a great challenge to improve the generalization ability of the face forgery detection algorithm. To address this challenge, we propose a general two-stream patch-based face forgery detection network (FDPT), which introduces a patch transformation to encourage the model to focus on stable information in different data. Specifically, a random transformation is designed to help CNN stream extract local subtle artifacts from images. Meanwhile, a sequence transformation is employed to enhance the global spatial representation ability of the image through the CNN-GRU stream. Finally, a fusion strategy is used to improve the detection accuracy. We conduct extensive experiments to show that FDPT achieves state-of-the-art performance on two popular benchmarks. Moreover, FDPT outperforms the recently proposed generalization methods when applied to forgery generated by unseen face manipulation techniques (e.g., 84.39% → 95.53% on Face2Face dataset). © 2021, Springer Nature Switzerland AG.

关键词： Benchmarking

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BlzFS: Crash Consistent Log-structured File System Based on Byte-loggable Zone for ZNS SSD

BlzFS: Crash Consistent Log-structured File System Based on ...

引用

IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Wenjie Qi Zhipeng Tan Ziyue Zhang Jing Zhang Chao Yu Ying Yuan Shikai Tan Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science & Technology Huazhong University of Science & Technology Guangdong Oppo Mobile Telecommunications Corp. Ltd

Emerging Zoned Namespace (ZNS) SSDs provide hosts with fine-grained, performance-predictable storage management. The ZNS SSD divides its address space into zones, and each zone must be written sequentially and cannot be overwritten. However, the write constraint results in frequent small modifications of file metadata to record the latest logical block addresses of the updated data blocks, increasing the overhead of fsync() system call. We comprehensively analyze the metadata overhead of fsync on a real ZNS SSD and find that (1) the update size of most metadata blocks (e.g., tens of bytes) is far less than the IO unit of the zone (e.g., 4 KiB), and frequent fsyncs cause severe metadata write amplification; (2) metadata block IOs significantly increase the fsync latency, decreasing the throughput of *** reduce the metadata overhead of fsync, we build the byte-loggable zone based on the persistent write cache of the opened zone, a novel extension of ZNS, which can be appended at byte granularity instead of block. Based on the byte-loggable zone, we present BlzFS, a high-performance and crash-consistent file system for ZNS SSD. Firstly, BlzFS maintains the update ranges of the metadata block by the update bitmap. Secondly, BlzFS ensures the persistence and consistency of metadata by logging the partial updates of metadata blocks to the device’s byte-loggable zone. Finally, BlzFS restores metadata consistency by replaying the metadata update log and scanning the list of recent allocated metadata blocks. In the evaluation, BlzFS shows a 33.5%∼96.1% reduction in metadata write traffic and a 1.37×∼1.91× throughput improvement compared with the original ZNS storage system.

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An AutoEncoder-Based Matrix Factorization Approach to Estimating Cell Proportion from Bulk Tumor RNA-seq data

An AutoEncoder-Based Matrix Factorization Approach to Estima...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingze Xu Yan Wang Xuping Xie Feilong Wang Qiong Chen Huiyan Sun College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781665429825

The deconvolution of infiltrating immune cells and stromal cells from complex tumor tissues is significant for studying the impact of these cells on tumor development, as well as assisting cancer therapies. Integrating cell-specific marker genes from several references, we put forward an AutoEncoder-based matrix factorization method to estimate the cell proportion in heterogeneous samples. The proportion predicted by our method achieved over 0.95 Pearson correlation coefficient (PCC) with the ground truth. Moreover, through analyzing association between cell proportion of tumor tissues and clinical information of the tumor patients, we found that the proportion of cancer-associated fibroblast (CAF) gradually increased with the progress of tumor, and the proportion of B cell in tissues was significantly related to the five-year survival rate of tumor patients.

关键词： Head Deconvolution Medical treatment Melanoma Fibroblasts Neck Reliability

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Deep Hashing with Triplet Loss of Hash Centers and Dissimilar Pairs for Image Retrieval

Deep Hashing with Triplet Loss of Hash Centers and Dissimila...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ye Liu Yan Pan Jian Yin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Artificial Intelligence Department Lizhi Inc. Beijing China Big Data Department Lizhi Inc. Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image hash coding learning based on deep neural network have a wide range of application scenarios and become a research hotspot in recent years. In the framework of most existing deep hashing methods, it is necessary to first design a loss function that can converge quickly, and then select an appropriate pre-training deep network and tune it. In order to balance global and local constraints, this paper combines the design of loss functions required by the processes of image feature learning and image hash coding, and adds the constraints of triple loss of hash centers and dissimilar pairs. The proposed novel loss function can give consideration to the characteristics of global hash centers, local dissimilarity of images and image classification labels in the training process. The pre-trained large models have been incorporated into the framework, which can extract image embeddings to assist in the generation of hash codes. The comparative experiments on several image datasets show that the DTLH method proposed in this paper can achieve better results than the traditional hashing methods and the deep hashing methods under different coding lengths.

关键词： Training Representation learning Image coding Codes Federated learning Computational modeling Image retrieval

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