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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Khoshdeli, Mina Winkelmaier, Garrett Parvin, Bahram Univ Nevada Biomed & Elect Engn Dept Reno NV 89557 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Nuclear segmentation is an important step in quantitative profiling of colony organization in 3D cell culture models. However, complexities arise from technical variations and biological heterogeneities. We proposed a new 3D segmentation model based on convolutional neural networks for 3D nuclear segmentation, which overcomes the complexities associated with non-uniform staining, aberrations in cellular morphologies, and cells being in different states. The uniqueness of the method originates from (i) volumetric operations to capture all the three-dimensional features, and (ii) the encoder-decoder architecture, which enables segmentation of the spheroid models in one forward pass. The method is validated with four human mammary epithelial cell (HMEC) lines-each with unique genetic makeup. The performance of the proposed method is compared with the previous methods and is shown that the deep learning model has a superior pixel-based segmentation, and an F1-score of 0.95 is reported.

关键词： Convolutional neural networks 3D nuclear segmentation encoder-decoder architecture 3D spheroid model volumetric convolution

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DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation

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IEEE ACCESS 2019年 7卷 169350-169358页

作者： Yuan, Jianzhong Zhou, Wujie Luo, Ting Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China Zhejiang Univ Inst Informat & Commun Engn Hangzhou 310027 Peoples R China Ningbo Univ Coll Sci & Technol Ningbo 315211 Peoples R China

Indoor scene segmentation is a difficult task in computer vision. We propose an indoor scene segmentation framework, called DFMNet, incorporating RGB and complementary depth information to establish indoor scene segmentation. We use the squeeze-and-excitation residual network as encoder to simultaneously extract features from RGB and depth data and fuse them in the decoder. Multiple average pooling layers and transposed convolution layers are used to process the encoded outputs and fuse their outputs over several decoder layers. To optimize the network parameters, we use a pyramid supervision training scheme, which applies supervised learning over different layers in the decoder to prevent vanishing gradients. We evaluated the proposed DFMNet on the NYU Depth V2 dataset, which consists of 1449 cluttered indoor scenes, achieving competitive results compared to state-of-the-art methods.

关键词： Image segmentation Decoding Convolution Feature extraction Data mining Fuses Computer architecture Indoor scene segmentation encoder-decoder architecture squeeze-and-excitation ResNet multiple average pooling layers pyramid supervision

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End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet plus

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REMOTE SENSING 2019年第11期11卷 1382-1382页

作者： Peng, Daifeng Zhang, Yongjun Guan, Haiyan Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Jiangsu Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

关键词： change detection deep learning end-to-end encoder-decoder architecture feature maps multiple side-outputs fusion

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Image Caption via Visual Attention Switch on DenseNet 6

Image Caption via Visual Attention Switch on DenseNet

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6th IEEE International Conference on Network Infrastructure and Digital Content (IEEE IC-NIDC)

作者： Hao, Yanlong Xie, Jiyang Lin, Zhiqing Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Syst Lab Beijing 100876 Peoples R China

ISBN: (纸本)9781538660676

We introduce a novel approach that is used to convert images into the corresponding language descriptions. This method follows the most popular encoder-decoder architecture. The encoder uses the recently proposed densely convolutional neural network (DenseNet) to extract the feature maps. Meanwhile, the decoder uses the long short time memory (LSTM) to parse the feature maps to descriptions. We predict the next word of descriptions by taking the effective combination of feature maps with word embedding of current input word by "visual attention switch". Finally, we compare the performance of the proposed model with other baseline models and achieve good results.

关键词： Image caption Visual attention switch encoder-decoder architecture DenseNet LSTM

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On the Adoption of Neural Networks in Modeling Software Reliability 2018

On the Adoption of Neural Networks in Modeling Software Reli...

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26th ACM Joint Meeting on European Software Engineering Conference (ESEC) / Symposium on the Foundations of Software Engineering (FSE)

作者： Gusmanov, Kamill Innopolis Univ Innopolis Russia

ISBN: (纸本)9781450355735

This work models the reliability of software systems using recurrent neural networks with long short-term memory (LSTM) units and truncated backpropagation algorithm, and encoder-decoder LSTM architecture and proposes LSTM with software reliability functions as activation functions and LSTM with input features as the output of software reliability functions. An initial evaluation on data coming from 4 industrial projects is also provided.

关键词： Software reliability modelling feedforward neural networks recurrent neural networks long short-term memory encoder-decoder architecture

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Arabic Machine Transliteration using an Attention-based encoder-decoder Model 3rd

Arabic Machine Transliteration using an Attention-based Enco...

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3rd Arabic Computational Linguistics Conference (ACLing)

作者： Ameur, Mohamed Seghir Hadj Meziane, Farid Guessoum, Ahmed USTHB Univ TALAA Grp Algiers Algeria Univ Salford Informat Res Ctr Salford M5 4WT Lancs England

Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been made towards improving this process for many languages such as English, French and Chinese, little research work has been accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal approach in comparison to some previous research developed in this area. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Natural Language Processing Arabic Language Arabic Transliteration Deep Learning Sequence-to-sequence Models encoder-decoder architecture Recurrent Neural Networks

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Adaptive Bus Encoding for Transition Reduction on Off-Chip Buses With Dynamically Varying Switching Characteristics

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2017年第11期25卷 3057-3066页

作者： Sarkar, Sumantra Biswas, Ayan Dhar, Anindya Sundar Rao, Rahul M. IIT Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India IBM India Private Ltd Bangalore 560045 Karnataka India

This paper presents an adaptive encoding framework for the reduction of transition activity in high-capacitance off-chip data buses, since power dissipation associated with those buses can be significant for high-speed communication. The technique relies on the observation of data characteristics over fixed window sizes and formation of cluster with bit lines having highly correlated switching patterns. The proposed method utilizes redundancy in space and time to prevent loss of information while retrieving data. We present analytical and experimental analyses, which demonstrate the activity reduction of our encoding scheme for various data. The extra power cost due to the encoder and decoder circuitry along with redundancy is offset due to reduced number of off-chip transitions.

关键词： Bus encoding schemes encoder-decoder architecture low power VLSI design off-chip bus

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SIMULTANEOUS SEGMENTATION AND CLASSIFICATION OF BIRD SONG USING CNN

SIMULTANEOUS SEGMENTATION AND CLASSIFICATION OF BIRD SONG US...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Narasimhan, Revathy Fern, Xiaoli Z. Raich, Raviv Oregon State Univ Sch EECS Corvallis OR 97331 USA

ISBN: (纸本)9781509041176

In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where the goal is to segment bird syllables from the recording and predict the bird species for the syllables. Traditional methods for this task addresses the segmentation and species prediction separately, leading to propagated errors. This work presents a new approach that performs simultaneous segmentation and classification of bird species using a Convolutional Neural Network (CNN) with encoder-decoder architecture. Experimental results on bird recordings show significant improvement compared to recent state-of-the-art methods for both segmentation and species classification.

关键词： Convolutional Neural Network encoder-decoder architecture bioacoustic species classification

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Arabic Machine Transliteration using an Attention-based encoder-decoder Model

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Procedia Computer Science 2017年 117卷 287-297页

作者： Mohamed Seghir Hadj Ameur Farid Meziane Ahmed Guessoum TALAA Group USTHB University Bab-Ezzouar Algiers Algeria Informatics Research Centre University of Salford M5 4WT United Kingdom

关键词： Natural Language Processing Arabic Language Arabic Transliteration Deep Learning Sequence-to-sequence Models encoder-decoder architecture Recurrent Neural Networks

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ABSTRACTIVE HEADLINE GENERATION FOR SPOKEN CONTENT BY ATTENTIVE RECURRENT NEURAL NETWORKS WITH ASR ERROR MODELING

ABSTRACTIVE HEADLINE GENERATION FOR SPOKEN CONTENT BY ATTENT...

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IEEE Workshop on Spoken Language Technology (SLT)

作者： Yu, Lang-Chi Lee, Hung-yi Lee, Lin-shan Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781509049035

Headline generation for spoken content is important since spoken content is difficult to be shown on the screen and browsed by the user. It is a special type of abstractive summarization, for which the summaries are generated word by word from scratch without using any part of the original content. Many deep learning approaches for headline generation from text document have been proposed recently, all requiring huge quantities of training data, which is difficult for spoken document summarization. In this paper, we propose an ASR error modeling approach to learn the underlying structure of ASR error patterns and incorporate this model in an Attentive Recurrent Neural Network (ARNN) architecture. In this way, the model for abstractive headline generation for spoken content can be learned from abundant text data and the ASR data for some recognizers. Experiments showed very encouraging results and verified that the proposed ASR error model works well even when the input spoken content is recognized by a recognizer very different from the one the model learned from.

关键词： abstractive summarization headline generation ASR error modeling attention mechanism encoder-decoder architecture

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