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Multi feature fusion paper classification model based on attention mechanism 基于注意机制的多特征融合纸张分类模型

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00063

C. Fan, Yongchun Li, Yuexin Wu

引用次数: 0

Alignment Offset Based Adaptive Training for Simultaneous Machine Translation 基于对齐偏移量的同步机器翻译自适应训练

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00035

Qiqi Liang, Yanjun Liu, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

引用次数: 0

Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy Image Segmentation 结合空间信息的自适应核证据c均值聚类在噪声图像分割中的应用

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00016

Lan Rong, Haowen Mi, Qu Na, Zhao Feng, Haiyan Yu, Zhang Lu

{"title":"Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy Image Segmentation","authors":"Lan Rong, Haowen Mi, Qu Na, Zhao Feng, Haiyan Yu, Zhang Lu","doi":"10.1109/ICNLP58431.2023.00016","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00016","url":null,"abstract":"Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. Visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76723762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Speech Recognition Based on Deep Learning 基于深度学习的语音识别研究综述

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00034

Youyao Liu, Jiale Chen, Jialei Gao, Shihao Gai

{"title":"A Survey of Speech Recognition Based on Deep Learning","authors":"Youyao Liu, Jiale Chen, Jialei Gao, Shihao Gai","doi":"10.1109/icnlp58431.2023.00034","DOIUrl":"https://doi.org/10.1109/icnlp58431.2023.00034","url":null,"abstract":"Artificial intelligence is the vane leading the world’s scientific and technological development and future lifestyle change in the 21st century, and speech recognition, as one of the indispensable technical means, is inevitably the focus of human attention. There are two problems in traditional speech recognition: first, speech recognition technology cannot be significantly improved, and second, speech recognition systems cannot accurately extract data and features. In order to solve these problems, this paper first compares the traditional speech recognition GMM-HMM model and establishes a DNN-HMM model, which proposes a method to improve the speed of speech recognition and greatly improves the recognition rate. However, DNN-HMM lacks the ability to use historical information to assist in the current task, and a second model is proposed on the basis of this problem, that is, the LSTM model is used to solve the problem of insufficient contextual information, which further improves the speech recognition ability. Then, in order to solve the problem of long memory loss and speed up training, the Transformer model is cited, and in order to solve the problem that the traditional language model can only predict the next word in one direction, the BERT model, which has a bidirectional language model, is invoked.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77048846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Siamese Network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update 基于GCT关注和双模板更新的Siamese网络视觉跟踪算法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00014

Sugang Ma, Siwei Sun, Lei Pu, Xiaobao Yang

{"title":"Siamese Network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update","authors":"Sugang Ma, Siwei Sun, Lei Pu, Xiaobao Yang","doi":"10.1109/ICNLP58431.2023.00014","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00014","url":null,"abstract":"To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese Network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network’s ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%; On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77884382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Post-encoding and contrastive learning method for response selection task 反应选择任务的后编码与对比学习方法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00050

Xianwei Xue, Chunping Li, Zhilin Lu, Youshu Zhang, Shanghua Xiao

{"title":"Post-encoding and contrastive learning method for response selection task","authors":"Xianwei Xue, Chunping Li, Zhilin Lu, Youshu Zhang, Shanghua Xiao","doi":"10.1109/ICNLP58431.2023.00050","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00050","url":null,"abstract":"Retrieval-based dialogue systems have achieved great performance improvements after the raise of pre-trained language models and Transformer mechanisms. In the process of context and response selection, the pre-trained language model can capture the relationship between texts, but current existing methods don’t consider the order of sentences and the relationship between the context and the response. At the same time, as the problem of a small number of positive samples in retrieval-based dialogue systems, it is difficult to train a learning model with high performance. In addition, existing methods usually requires the larger computational cost after splicing the context and the response. To solve the above problems, we propose a post-encoding approach combining with the strategy of contrastive learning. The order of the context and the relationship between sentences in dialogues and response are reflected in the encoding process, and a new loss function is designed for contrastive learning. The propose approach is validated through experiments on public datasets. The experiment results show that our model achieves better performance and effectiveness compared to existing methods.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87773012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholding for Color Image Segmentation 基于自适应slic的模糊强度不相似阈值分割彩色图像

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00017

Lan Rong, Danlin Feng, Zhao Feng, Haiyan Yu, Zhang Lu

引用次数: 0

Assessment of Nonverbal-behavior Annotation Tags in Multimodal Learner Corpus 多模态学习者语料库中非语言行为标注标签的评价

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00046

Katsunori Kotani, T. Yoshimi

引用次数: 0

A Graph Autoencoder-based Anomaly Detection Method for Attributed Networks 基于图自编码器的属性网络异常检测方法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00067

Kunpeng Zhang, Guangyue Lu, Yuxin Li, Cai Xu

{"title":"A Graph Autoencoder-based Anomaly Detection Method for Attributed Networks","authors":"Kunpeng Zhang, Guangyue Lu, Yuxin Li, Cai Xu","doi":"10.1109/ICNLP58431.2023.00067","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00067","url":null,"abstract":"Anomaly detection in attributed networks aims to find anomalous nodes in the network that differ from the behavior pattern of most nodes, and graph neural network provide a way to use fused structural and attribute information. However, existing methods based on Graph Convolutional Network (GCN) detection do not consider the over-smoothing phenomenon of GCN due to the stacks of network layers, which causes significant performance deterioration. To address the above problems, we propose a graph autoencoder-based anomaly detection method for attributed networks: Residual Graph Autoencoder (Res-GAE), by which the performance is effectively improved. Res-GAE contains an encoder and two decoders. More specifically, the encoder consists of a GCN and a residual network is utilized to learn the network representation. The decoders are designed to reconstruct the network structure and node attributes respectively. After that, the objective function is used to analyze the reconstruction error to generate the anomaly score ranking, to realize anomaly detection. Extensive experiments on the three datasets (BlogCatalog, Flickr, ACM) demonstrate that the proposed method has the significant improvement compared with other baseline methods.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89892466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Construction and Performance Analysis of Combinatorial Chaotic Map Based on Fuzzy Entropy 基于模糊熵的组合混沌映射构造及性能分析

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00021

Tingting Chen, Xiaodong Zhang, Meixia Miao, Pengfei Tu

引用次数: 0