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DE-ESD: dual encoder-based entity synonym discovery using pre-trained contextual embeddings

EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Huang, Subin Chen, Junjie Yu, Chengzhen Li, Daoyu Zhou, Qing Liu, Sanmin AnHui Polytech Univ Sch Comp & Informat Wuhu 241000 Anhui Peoples R China

Extracting synonymous entities from unstructured text is important for enhancing entity-dependent applications such as web searches and question-answering systems. Existing work primarily falls into two types: statistics- based and deep learning-based. However, these approaches often fail to discern fine semantic nuances among entity mentions and are prone to cumulative errors;thus, they inadequately represent the holistic semantics of entity synonym sets. To address these limitations, this paper introduces a novel framework, dual encoder-based Entity Synonym Discovery (DE-ESD). The proposed method initially uses pre-trained language models to extract multiperspective contextual embeddings of entity mentions. Then, it employs a dual encoder architecture to differentiate features between an established entity synonym set and a pseudo-set-created by adding a candidate entity mention to the synonym set. A set scorer evaluates the quality scores of both sets. By leveraging the trained dual encoder and the set scorer, DE-ESD can implement an efficient online algorithm for mining new entity synonym sets for open text streams. The experimental results obtained on two real-world datasets (NYT and Wiki) demonstrate the effectiveness of DE-ESD. Furthermore, we investigated the impact of different pre-trained language models on DE-ESD performance, particularly their ability to extract effective contextual embeddings.

关键词： Entity synonym set Entity synonym discovery dual encoder Pre-trained language model Contextual embedding

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Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Ahmed, Asaad Sun, Guangmin Bilal, Anas Li, Yu Ebad, Shouki A. Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou 571158 Peoples R China Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. DuaSkinSeg leverages a pre-trained MobileNetV2 for efficient local feature extraction. Subsequently, a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder architecture extracts higher-level features focusing on long-range dependencies. This approach aims to combine the efficiency of MobileNetV2 with the feature extraction capabilities of the ViT encoder for improved segmentation performance. To evaluate DuaSkinSeg's effectiveness, we conducted experiments on three publicly available benchmark datasets: ISIC 2016, ISIC 2017, and ISIC 2018. The results demonstrate that DuaSkinSeg achieves competitive performance compared to existing methods, highlighting the potential of the dual encoder architecture for accurate skin lesion segmentation.

关键词： Skin Lesion Segmentation dual encoder Vision Transformer (ViT) Convolutional Neural Networks (CNNs) ViT-CNN

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dual-encoder model for typhoon path prediction with multiscale spatiotemporal data fusion

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Earth Science Informatics 2025年第2期18卷 1-17页

作者： Ren, Shuxia Zhong, Ruikun Guo, Zewei Zhang, Zining School of Software Engineering Tiangong University Tianjin 300387 China School of Computer Science and Technology Tiangong University Tianjin 300387 China

Extreme weather caused by typhoons poses a severe threat to human life safety and socio-economic development, making accurate prediction of typhoon paths crucial. However, existing prediction models struggle to effectively handle and integrate heterogeneous data, overlooking deep correlations between the data, which in turn affects the accuracy of the models. This paper proposes a dual-encoder Spatiotemporal Fusion Model for Typhoon Path Prediction (DESF-Typhoon), designed to effectively integrate data from different time scales and extract the underlying relationships between the data, thereby improving prediction accuracy. Specifically, we designed a dual-encoder module that effectively captures complex nonlinear data structures, accounting for the intricate influences of factors such as geography and environment on path, while integrating spatial features of typhoon at different time scales. Subsequently, we introduced a feature interaction module to explore the relationships between different features, which can adaptively learn the interaction weights between them, resulting in a richer feature representation. We evaluated the model using the dataset from the China Meteorological Administration (CMA) and compared its performance with traditional prediction methods and deep learning-based approaches. The results demonstrate that the model significantly improves accuracy and robustness, making innovative contributions in data fusion and spatiotemporal feature modeling. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： dual encoder Feature interaction Spatiotemporal data Tropical cyclone prediction

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dual encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第8期41卷 1975-1989页

作者： Li, Yang Zhang, Yue Cui, Weigang Lei, Baiying Kuang, Xihe Zhang, Teng Beihang Univ Sch Automat Sci & Elect Engn Beijing 100190 Peoples R China Shenzhen Univ Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen 518060 Guangdong Peoples R China Univ Hong Kong Fac Med Dept Orthopaed & Traumatol Hong Kong Peoples R China

Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.

关键词： Image edge detection Biomedical imaging Image segmentation Feature extraction Deep learning Convolution Decoding Retinal vessel segmentation dual encoder dynamic graph convolution network edge enhancement deep learning

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SN-Unetformer: a dual encoder hybrid architecture for complex targets in high-resolution remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2023年第2期17卷

作者： Zhu, Xiaotong Peng, Taile Hu, Xiaobin Guo, Jia Cao, Taotao Wang, Hao Huaibei Normal Univ Coll Comp Sci & Technol Huaibei Peoples R China Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei Peoples R China

Accurately identifying the semantic information of complex objects is a challenging problem in semantic segmentation of remote sensing images. We propose a bi-encoder network for semantic segmentation of complex targets, called the SN-Unetformer. It combines ConvNeXt and Swin Transformer into a bi-encoder and constructs a feature fusion module (FFM) to fully integrate the semantic information of the bi-encoder by exploiting channel dependence. Moreover, an efficient attention mechanism has been introduced to model the global-local relationship. To the best of our knowledge, our proposed network is innovative, as it is the first method to combine two popular networks, ConvNeXt and the Swin Transformer, into a dual encoder. Our SN-Unetformer has been tested on large-scale Vaihingen and Potsdam datasets, as well as the LoveDA dataset, with significant challenges. Compared to current advanced methods for semantic segmentation for remote sensing images, our accuracy is significantly better. In particular, our method achieves 84.3% of mean intersection over union on the Vaihingen dataset, which is the best result currently available for this dataset. (c) 2023 Society of PhotoOptical Instrumentation Engineers (SPIE) [DOI: 10.1117/***.17.026512]

关键词： semantic segmentation remote sensing Swin Transformer ConvNeXt dual encoder

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A Lightweight Network with dual encoder and Cross Feature Fusion for Cement Pavement Crack Detection

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 255-273页

作者： Zhong Qu Guoqing Mu Bin Yuan School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr

关键词： Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection

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dual encoder Attention U-net for nuclei segmentation 43

Dual Encoder Attention U-net for nuclei segmentation

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Vahadane, Abhishek Atheeth, B. Majumdar, Shantanu Rakuten Inc Rakuten Inst Technol India Tokyo Japan

ISBN: (纸本)9781728111797

Nuclei segmentation in whole slide images (WSIs) stained with Hematoxylin and Eosin (H&E) dye, is a key step in computational pathology which aims to automate the laborious process of manual counting and segmentation. Nuclei segmentation is a challenging problem that involves challenges such as touching nuclei resolution, small-sized nuclei, size, and shape variations. With the advent of deep learning, convolution neural networks (CNNs) have shown a powerful ability to extract effective representations from microscopic H&E images. We propose a novel dual encoder Attention U-net (DEAU) deep learning architecture and pseudo hard attention gating mechanism, to enhance the attention to target instances. We added a new secondary encoder to the attention U-net to capture the best attention for a given input. Since H captures nuclei information, we propose a stain-separated H channel as input to the secondary encoder. The role of the secondary encoder is to transform attention prior to different spatial resolutions while learning significant attention information. The proposed DEAU performance was evaluated on three publicly available H&E data sets for nuclei segmentation from different research groups. Experimental results show that our approach outperforms other attention-based approaches for nuclei segmentation.

关键词： dual encoder Nuclei segmentation Attention

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DETA-Net: A dual encoder Network with Text-Guided Attention Mechanism for Skin-Lesions Segmentation 1

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Shen, Cong Wang, Xinyue Tang, Jijun Liao, Zhijun Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China Minist Educ Engn Res Ctr Learning Based Intelligent Syst Tianjin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC USA Fujian Med Univ Sch Basic Med Sci Fuzhou Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Skin-lesions segmentation plays a prominent role in computer-aided diagnosis systems for skin cancer, especially the remarkable success of the convolutional neural network (CNN) approaches in skin-lesions segmentation. However, it faces intractable challenges such as variable shape and blurred skin lesions boundaries. To this end, past research has employed cutting-edge mechanisms, including diverse attention modules. Inspired by state-of-the-art works, this study proposed a dual encoder framework with a Text-Guided Attention Network (DETA-Net) which can accurately and efficiently segment various and blurred lesions. Firstly, we designed a multi-scale joint encoder that took the advantage of both the CNNs and Transformer to extract features under the blurred lesion background condition. In addition, we introduced text-guided attention to propel classification in the manner of text-based embedding in the DETA-Net so that the variation in the size and number of the lesion can be efficiently accommodated. Experimental results demonstrated that DETA-Net provided better performance across multiple datasets compared with state-of-the-art on variable-sized skin lesion datasets in Skin-Cancer detection. We also evaluated the effectiveness of DETA-Net through extensive ablation studies on three different datasets, including ISIC 2016, ISIC 2018, and PH2 datasets. The baseline achieved 0.8838 Dice on ISIC 2016, 0.8864 Dice on ISIC 2018, and 0.8695 Dice on PH2.

关键词： Skin Cancer dual encoder Medical Image Segmentation Text-guided

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A semantically enhanced dual encoder for aspect sentiment triplet extraction

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NEUROCOMPUTING 2023年 562卷

作者： Jiang, Baoxing Liang, Shehui Liu, Peiyu Dong, Kaifang Li, Hongye Shandong Normal Univ Sch Informat Sci & Engn Jinan 235000 Peoples R China Nanjing Normal Univ Int Coll Chinese Studies Nanjing 210097 Peoples R China

Aspect sentiment triplet extraction (ASTE) is a crucial subtask of aspect-based sentiment analysis (ABSA) that aims to comprehensively identify sentiment triplets. Previous research has focused on enhancing ASTE through innovative table-filling strategies. However, these approaches often overlook the multi-perspective nature of language expressions, resulting in a loss of valuable interaction information between aspects and opinions. To address this limitation, we propose a framework that leverages both a basic encoder, primarily based on BERT, and a particular encoder comprising a Bi-LSTM network and graph convolutional network (GCN). The basic encoder captures the surface-level semantics of linguistic expressions, while the particular encoder extracts deeper semantics, including syntactic and lexical information. By modeling the dependency tree of comments and considering the part-of-speech and positional information of words, we aim to capture semantics that are more relevant to the underlying intentions of the sentences. An interaction strategy combines the semantics learned by the two encoders, enabling the fusion of multiple perspectives and facilitating a more comprehensive understanding of aspect-opinion relationships. Experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our proposed framework.

关键词： Aspect sentiment triplet extraction Triplet extraction dual encoder Encoding interaction

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An effective dual encoder network with a feature attention large kernel for building extraction

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GEOCARTO INTERNATIONAL 2024年第1期39卷

作者： Qiu, Shaobo Zhou, Jingchun Liu, Yuan Meng, Xiangrui Yunnan Normal Univ Fac Geog Kunming Yunnan Peoples R China

Transformer models boost building extraction accuracy by capturing global features from images. However, convolutional networks' potential in local feature extraction remains underutilized in CNN + Transformer models, limiting performance. To harness convolutional networks for local feature extraction, we propose a feature attention large kernel (ALK) module and a dual encoder network for high-resolution image-building extraction. The model integrates an attention-based large kernel encoder, a ResNet50-Transformer encoder, a Channel Transformer (Ctrans) module and a decoder. Efficiently capturing local and global building features from both convolutional and positional perspectives, the dual encoder enhances performance. Moreover, replacing skip connections with the CTrans module mitigates semantic inconsistency during feature fusion, ensuring better multidimensional feature integration. Experimental results demonstrate superior extraction of local and global features compared to other models, showcasing the potential of enhancing local feature extraction in advancing CNN + Transformer models.

关键词： Buildings image semantic segmentation dual encoder feature attention large kernel

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