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arXiv 2022年

作者： Lei, Xiaochun Mai, Weiliang Xie, Junlin Liu, He Jiang, Zetao Gong, Zhaoting Lu, Chang Lu, Linjun School of Computer Science and Information Security Guilin University of Electronic Technology Jinji Road Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Jinji Road Guangxi Guilin541004 China

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Chinese Summary Generation Algorithm Based on Contrastive Attention Mechanism

Chinese Summary Generation Algorithm Based on Contrastive At...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Liupeng Guo Yannan Xiao Yayuan Wen Peizhi Wen School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Electronic and Information Engineering/School of Integrated Circuits GUANGXI Normal University Guilin China

Generative summarization based on the seq2seq model has gained widespread adoption due to the advancements and efficacy of the sequence-to-sequence framework. However, these models encounter challenges in handling out-of-vocabulary words and may produce summaries with factual inaccuracies. This paper addresses these issues by proposing a summarization generation model, MBC, which is built upon the Transformer as the baseline model. Additionally, it incorporates the BERT pre-trained model, pointer generation network, contrastive attention mechanism, and coverage loss. The model employs a pointer generation network to augment its vocabulary coverage capabilities by generating new terms. To enhance the representation of key information from the source text, a contrastive attention mechanism is employed during summary generation. In addition, by introducing coverage loss, the model can reduce repetitive content in the summary. To assess the model’s performance, this paper employs ROUGE values as evaluation metrics and conducts comparative experiments with other models using the LCSTS dataset. The experimental results demonstrate that, in comparison with other models, this model is more adept at attending to salient information within the source text and generating summaries of greater accuracy.

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A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation

A Lightweight Chinese Summarization Generation Method Based ...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Shiyue Liu Yannan Xiao Xueyong Wang Wenming Huang Zhenrong Deng Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models.

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Teeth Yolact: A Model Fusing Deep-Shallow, Local-Global And Edge Features

SSRN

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SSRN 2024年

作者： Zhou, Tao Wang, Yaxing Chai, Wenwen Pan, Yunfeng Zhang, Zhe Lu, Huiling Xia, Yong School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information and Engineering Ningxia Medical University Yin Chuan750004 China Key Laboratory of Image and Graphics Intelligent Processing State Ethnic Affairs Commission North Minzu University Yinchuan750021 China School of Computer Northwestern Polytechnical University Xi’an710072 China

Background and ObjectiveThe instance segmentation of impacted tooth in the oral panoramic X-ray images is research hot. However, impacted tooth in panoramic X-Ray images lead to teeth deformities, low contrast between the tooth and periodontal tissue. In this paper, a Teeth YOLACT instance segmentation model is *** main contributions of this paper are as follows: Firstly, a Multi-scale Res-Transformer Module (MRTM) is designed, in the module, the depth-wise separable convolution with different perceptive fields are used to improve the model sensitivity to the lesion size, the Vision Transformer are used to improve the model perception ability about global features. Secondly, the Context Interaction-awareness Module (CIaM) is designed to fuse deep and shallow features. The shallow spatial features are guided by the deep semantic features. Then the shallow spatial features are embedded into the deep semantic features, and the cross-weighted attention mechanism is used to efficiently aggregate the deep and shallow features, richer context information are obtained. Thirdly, the Edge-preserving perception Module(E2PM) is designed to enhance the teeth edge features. The first order differential operator is used to get the tooth edge weight, perception ability of tooth edge features is improved. The shallow spatial feature is fused by linear mapping, weight concatenation and matrix multiplication operations to preserve the tooth edge information. Finally, comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image *** results show that the APdet, APseg, ARdet, ARseg, mAPdet and mAPseg indicators of the proposed model are 89.9%, 91.9%, 77.4%, 77.6%, 72.8% and 73.5%, *** are some positive significances about the dental computer-aided diagnosis based on panoramic oral X-ray images. © 2024, The Authors. All rights reserved.

关键词： Computer aided diagnosis

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Binary Representation via Jointly Personalized Sparse Hashing

arXiv

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arXiv 2022年

作者： Wang, Xiaoqin Chen, Chen Lan, Rushi Liu, Licheng Liu, Zhenbing Zhou, Huiyu Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China College of Electrical and Information Engineering Hunan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g. color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, firstly, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our proposed JPSH, we incorporate the proposed PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters, but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. We apply the proposed representation learning algorithm JPSH to the similarity search task. Extensive experiments on four benchmark datasets verify that the proposed JPSH outperforms several state-of-the-art unsupervised hashing algorithms. Cop

关键词： Hash functions

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Metaverse: Security and Privacy Concerns

arXiv

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arXiv 2022年

作者： Zhao, Ruoyu Zhang, Yushu Zhu, Youwen Lan, Rushi Hua, Zhongyun The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Shenzhen518055 China

The term "metaverse", a three-dimensional virtual universe similar to the real realm, has always been full of imagination since it was put forward in the 1990s. Recently, it is possible to realize the metaverse with the continuous emergence and progress of various technologies, and thus it has attracted extensive attention again. It may bring a lot of benefits to human society such as reducing discrimination, eliminating individual differences, and socializing. However, everything has security and privacy concerns, which is no exception for the metaverse. In this article, we firstly analyze the concept of the metaverse and propose that it is a super virtual-reality (VR) ecosystem compared with other VR technologies. Then, we carefully analyze and elaborate on possible security and privacy concerns from four perspectives: user information, communication, scenario, and goods, and immediately, the potential solutions are correspondingly put forward. Meanwhile, we propose the need to take advantage of the new buckets effect to comprehensively address security and privacy concerns from a philosophical perspective, which hopefully will bring some progress to the metaverse community. Copyright © 2022, The Authors. All rights reserved.

关键词： Virtual reality

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A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

arXiv

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arXiv 2022年

作者： Wang, Huadeng Liu, Zhipeng Lan, Rushi Liu, Zhenbing Luo, Xiaonan Pan, Xipeng Li, Bingbing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Department of Pathology Ganzhou Municipal Hospital Ganzhou341000 China

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://***/antifen/mitosis-nuclei-segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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Thought expression analysis of english essays combining opinion and sentiment

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Journal of Computers (Taiwan) 2021年第3期32卷 43-56页

作者： Wang, Yabing Huang, Guimin Zhang, Xiaowei Li, Yiqun Li, Maolin Li, Hui Li, Jun Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

With the rapid development of science, technology and the gradual information of education, the automatic English essays correction system has received more and more attention. The existing automatic English essays correction system can provide feedback on vocabulary and grammar, but it has a lack of thought expression analysis of English essays. Therefore, it is impossible to comprehensively evaluate whether opinion of English essays is relevant and expression of sentiment is clear. To solve this problem, we propose to analyze the thought expression of English essays by combining sentiment analysis and opinion analysis. Through experiment analysis and verification from the aspects of relevance degree of opinion, sentiment tendency, and thought expression of English essays, the results show the proposed method has a good performance in analyzing thought expression of English essays. © 2021 Computer Society of the Republic of China. All rights reserved.

关键词： Sentiment analysis

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A Skeleton Posture Transfer Method from Kinect Capture

A Skeleton Posture Transfer Method from Kinect Capture

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Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI), International Conference on

作者： Zhengjie Deng Shuqian He Mengyan Zhang Yizhen Wang School of information science and technology Hainan Normal University Haikou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of physics and electronic engineering Hainan Normal University Haikou China

At present, 3D models are applied to many fields, and the action control of 3D model needs be studied further. In order to control a 3D model using user-acceptable devices, this paper proposes a method to transfer the posture of the skeleton captured by Kinect to a skeleton of the Soldier model, which is a sample in Microsoft's DirectX SDK. First, using the Kinect to collect the user's skeleton information, combined with the initial posture of the Soldier skeleton, through node matching, inverse mirror transformation, transformation from absolute to relative, coordinate space transformation, to generate a skeleton with the same structure as the Soldier skeleton, which can be applied to the Soldier model. And in a virtual reality scene, the corresponding actions of users can drive the Soldier model. Experiments show that the method is feasible and effective. The proposed method can also be referenced and applied to a target skeleton with other structures.

关键词： Human computer interaction Solid modeling Three-dimensional displays Virtual reality Skeleton Mirrors Artificial intelligence

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Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN 8

Named Entity Recognition in Chinese Judicial Domain Based on...

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8th International Conference on Digital Home, ICDH 2020

作者： Huang, Wenming Zhang, Juan Xiao, Yannan Han, Zheng Deng, Zhenrong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin China Guilin Bank Guilin China

ISBN: (纸本)9781728192345

Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field. © 2020 IEEE.

关键词： Semantics

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