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An encoder-decoder Approach to Handwritten Mathematical Expression Recognition with Multi-head Attention and Stacked decoder 16th

An Encoder-Decoder Approach to Handwritten Mathematical Expr...

引用

16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Ding, Haisong Chen, Kai Huo, Qiang Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9783030863319

encoder-decoder framework with attention mechanism has become a mainstream solution to handwritten mathematical expression recognition (HMER) since "watch, attend and parse (WAP)" approach was proposed in 2017, where a convolutional neural network is used as encoder and a gated recurrent unit with attention is used in decoder. Inspired by the recent success of Transformer in many applications, in this paper, we adopt the design of multi-head attention and stacked decoder in Transformer to improve the decoder part of the WAP framework for HMER. Experimental results on CROHME tasks show that multi-head attention can boost the expression recognition rate (ExpRate) of WAP from 54.32%/58.05% to 56.76%/59.72% and stacked decoder can further improve ExpRate to 57.72%/61.38% on CROHME 2016/2019 test sets.

关键词： encoder-decoder Multi-head attention Stacked decoder Transformer Mathematical expression recognition

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Table Structure Recognition Using CoDec encoder-decoder 16th

Table Structure Recognition Using CoDec Encoder-Decoder

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Pegu, Bhanupriya Singh, Maneet Agarwal, Aakash Mitra, Aniruddha Singh, Karamjit Mastercard AI Garage Gurgaon India

ISBN: (纸本)9783030861599;9783030861582

Automated document analysis and parsing has been the focus of research since a long time. An important component of document parsing revolves around understanding tabular regions with respect to their structure identification, followed by precise information extraction. While substantial effort has gone into table detection and information extraction from documents, table structure recognition remains to be a long-standing task demanding dedicated attention. The identification of the table structure enables extraction of structured information from tabular regions which can then be utilized for further applications. To this effect, this research proposes a novel table structure recognition pipeline consisting of row identification and column identification modules. The column identification module utilizes a novel Column Detector encoder-decoder model (termed as CoDec encoder decoder) which is trained via a novel loss function for predicting the column mask for a given input image. Experiments have been performed to analyze the different components of the proposed pipeline, thus supporting their inclusion for enhanced performance. The proposed pipeline has been evaluated on the challenging ICDAR 2013 table structure recognition dataset, where it demonstrates state-of-the-art performance.

关键词： Table structure recognition encoder-decoder Document analysis

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DeepSEED: 3D Squeeze-and-Excitation encoder-decoder Convolutional Neural Networks for Pulmonary Nodule Detection 17

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolut...

引用

IEEE 17th International Symposium on Biomedical Imaging (ISBI)

作者： Li, Yuemeng Fan, Yong Univ Penn Ctr Biomed Image Comp & Analyt Perelman Sch Med Dept Radiol Philadelphia PA 19104 USA

ISBN: (纸本)9781538693308

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule detection methods, we develop a novel deep 3D convolutional neural network with an encoder-decoder structure in conjunction with a region proposal network. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the false positive rate and combat the sample imbalance problem associated with nodule detection. We adopt the squeeze-and-excitation structure to learn effective image features and utilize inter-dependency information of different feature maps. We have validated our method based on publicly available CT scans with manually labelled ground-truth obtained from LIDC/IDRI dataset and its subset LUNA16 with thinner slices. Ablation studies and experimental results have demonstrated that our method could outperform state-of-the-art nodule detection methods by a large margin.

关键词： Deep convolutional networks squeeze-and-excitation encoder-decoder lung nodule detection

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Multi-scale Salient Instance Segmentation based on encoder-decoder 13

Multi-scale Salient Instance Segmentation based on Encoder-D...

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13th Asian Conference on Machine Learning (ACML)

作者： Chen, Houru Shi, Caijuan Li, Wei Duan, Changyu Yan, Jinwei North China Univ Sci & Technol Tangshan 063210 Hebei Peoples R China

Salient instance segmentation refers to segmenting noticeable instance objects in images. In the face of multi-scale salient instances and overlapping instances, the existing salient instance segmentation methods have great limitations including inaccurate detection of large-scale instances, missing detection of small-scale instances, and wrong segmentation of overlapping in-stances. In order to solve these problems, a new multi-scale salient instance segmentation network (MSISNet) based on encoder-decoder is proposed. Firstly, we design a receptive field encoder (RFE), which adopts the serial dilated convolution instead of parallel dilated convolution and utilizes some common tricks to achieve better precision and speed. RFE can alleviate the problems of inaccurate detection of large-scale instances, missing detection of small-scale instances, and especially wrong segmentation of overlapping instances. Then, a pyramid decoder (PD) for the detection branch is designed to further alleviate the problem of inaccurate detection of large-scale instances and the difficulty in locating small-scale instances. Finally, a multi-stage decoder (MSD) is designed to improve the quality of the segmentation mask. In order to sufficiently evaluate the generalizability of our method, experiments are conducted not only on Salient Instance Segmentation-1K (SIS-1K) dataset, but also on Salient Objects in Clutter (SOC) dataset. The results show that the proposed method MSISNet is superior to the existing salient instance segmentation methods on mAP0:5 and some recently proposed non-salient instance segmentation methods.

关键词： Salient instance segmentation encoder-decoder Multi-scale Receptive field Feature fusion

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DHA-Net: An encoder-decoder network fusing multi-scale features for optic disc segmentation

DHA-Net: An encoder-decoder network fusing multi-scale featu...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Rising Above Covid-19

作者： Zheng, Xuan He, Yi Yuan, Huaqing Jiang, Yanglin Xu, Yanbin Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China TianJin Eye Hosp Tianjin Peoples R China

ISBN: (纸本)9781665453837

Automatic and accurate segmentation of the optic disc (OD) region has practical applications in the medical field. In this study, a novel encoder-decoder network is proposed to segment the ODs automatically and accurately. The encoder consists of three parts: (1) A low-level feature extraction module composed of dense connectivity block (Dense Block) which can output rich low-level features;(2) A High-resolution Block (HR Block) which can extract sufficient semantic information while reducing parameters;(3) An Atrous Spatial Pyramid Pooling (ASPP) module is used to obtain high-level features. Therefore, the network is named DHA-Net. The proposed decoder takes advantage of the multi-scale features from the encoder to predict OD regions. Compared with the existing methods on three datasets, it is proved that the proposed method is better than the current excellent methods in the segmentation results of normal and abnormal fundus. The ablation studies proved the influence of each module on the segmentation performance, and explained the network structure reasonably. In the case of fewer network parameters, DHA-Net achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC) and other evaluation metrics. DHA-Net is lightweight and can use multi-scale features to predict OD regions.

关键词： Medical image segmentation Convolutional neural network Optic disc encoder-decoder Multi-scale features

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Comparison of encoder-decoder Networks for Soccer Field Segmentation 20

Comparison of Encoder-Decoder Networks for Soccer Field Segm...

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20th Latin American Robotics Symposium (LARS) / 15th Brazilian Symposium on Robotics (SBR) / 14th Workshop on Robotics in Education (WRE)

作者： Guimaraes, Otavio H. R. Maximo, Marcos R. O. A. Parente de Oliveira, Jose Maria Ecole Polytech Inst Polytech Paris Route Saclay F-91128 Palaiseau Ile De France France Aeronaut Inst Technol Comp Sci Div Autonomous Computat Syst Lab Praca Marechal Eduardo Gomes 50 BR-12228900 Sao Jose Dos Campos SP Brazil Aeronaut Inst Technol Comp Sci Div Praca Marechal Eduardo Gomes 50 BR-12228900 Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9798350315387

Convolutional neural networks consist of state-of-the-art models used for the solution of computer vision problems. This paper contributes by evaluating the efficiency of several encoder-decoder neural networks, trained to perform the segmentation of the soccer field in Humanoid KidSize Robot Soccer competitions. To compare the efficiency of several encoders, a total of fourteen neural network models, based on the U-Net and SegNet architectures, were tested and compared in terms of accuracy, cost function value, IoU, and average inference time. Based on that, the networks based on U-Net that utilized the MobileNetv3Small or the ResNet18 for the encoding process were found to be the optimal solution among the considered alternatives to segment the soccer field.

关键词： neural networks CNN encoder-decoder semantic segmentation robot soccer

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Multi-Shell D-MRI Reconstruction via Residual Learning utilizing encoder-decoder Network with Attention (MSR-Net) 42

Multi-Shell D-MRI Reconstruction via Residual Learning utili...

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42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Jha, Ranjeet Ranjan Nigam, Aditya Bhavsar, Arnav Pathak, Sudhir K. Schneider, Walter Rathish, K. Indian Inst Technol Mandi Sch Comp & Elect Engn SCEE Mandi Himachal Prades India Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA 15260 USA Indian Inst Technol Kanpur Dept Math & Stat Kanpur Uttar Pradesh India

ISBN: (纸本)9781728119908

Contemporary diffusion MRI based analysis with HARDI, which provides more accurate fiber orientation, can be performed using single or multiple b-values (single or multi-shell). Single shell HARDI cannot provide volume fraction for different tissue types, which can produce bias and noisier results in estimation of fiber ODF. Multi-shell acquisition can resolve this issue. However, it requires more scanning time and is therefore not very well suited in clinical setting. Considering this, we propose a novel deep learning architecture, MSR-Net, for reconstruction of diffusion MRI volumes for some b-value using acquisitions at another b-value. In this work, we demonstrate this for b = 2000 s/mm(2) and b = 1000 s/mm(2). We learn such a transformation in the space of spherical harmonic coefficients. The proposed network consists of encoder-decoder along-with an attention module and a feature module. We have considered L2 and Content loss for optimizing and improving the performance. We have trained and validated the network using the HCP data-set with standard qualitative and quantitative performance measures.

关键词： Diffusion MRI Multi-shell HARDI encoder-decoder Content Loss Attention module Feature module

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An encoder-decoder Method with Position-Aware for Printed Mathematical Expression Recognition 17th

An Encoder-Decoder Method with Position-Aware for Printed Ma...

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17th International Conference on Document Analysis and Recognition (ICDAR)

作者： Long, Jun Hong, Quan Yang, Liu Cent South Univ Sch Comp Sci & Engn Changsha 410075 Hunan Peoples R China Cent South Univ Big Data Inst Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9783031416750;9783031416767

Printed mathematical expression recognition is to transform printed mathematical formula image into LaTeX sequence. Recently, many methods based on deep learning have been proposed to solve this task. However, the positional relationship between mathematical symbols is often ignored or represented insufficient, leading to the loss of structural features of mathematical formulas. To overcome this challenge, we propose a position-aware encoder-decoder model for printed mathematical expression recognition. We design a two-dimensional position encoding algorithm based on sin/cos function to capture positional relationship between mathematical symbols. Meanwhile, we adopt a more advanced image feature extraction network. In decoder component, we use Bi-GRU as the translator, and add attention mechanism to make decoder focus on the important local information. We conduct experiments on the public dataset IM2LaTeX-100K, and the results show that our proposed approach is more excellent than the majority of advanced methods.

关键词： Deep learning Mathematical expression recognition encoder-decoder Position encoding Attention mechanism

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Online encoder-decoder Anomaly Detection using encoder-decoder Architecture with Novel Self-configuring Neural Networks & Pure Linear Genetic Programming for Embedded Systems 11th

Online Encoder-decoder Anomaly Detection using Encoder-decod...

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11th International Joint Conference on Computational Intelligence (IJCCI)

作者： Kasparaviciute, Gabriele Thelin, Malin Nordin, Peter Soderstam, Per Magnusson, Christian Almljung, Mattias Chalmers Univ Technol Chalmersplatsen 4 Gothenburg Sweden Univ Gothenburg Rannvagen 6B Gothenburg Sweden Semcon AB Lindholmsallen 2 Gothenburg Sweden

ISBN: (纸本)9789897583841

Recent anomaly detection techniques locus on the use of neural networks and an encoder-decoder architecture. However, these techniques lead to trade offs if implemented in an embedded environment such as high heat management, power consumption and hardware costs. This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus. The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP). Second method uses self-configuring neural network that is created using evolutionary algorithm paradigm learning both architecture and weights suitable for embedded systems. Both approaches have the following advantages: it is inexpensive regarding resource use, can be run on almost any embedded board due to linear register machine advantages in computation. The proposed methods are also faster by at least one order of magnitude, and it includes both inference and complete training.

关键词： encoder-decoder Anomaly Detection Linear Genetic Programming Evolutionary Algorithm Genetic Algorithm Embedded Self-configuring Neural Network

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Automatic Correction of Speech Recognized Mathematical Equations using encoder-decoder Attention Model 19

Automatic Correction of Speech Recognized Mathematical Equat...

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19th IEEE-India-Council International Conference (INDICON)

作者： Mounika, Y. Tarakaram, Y. Prasanna, Y. Lakshmi Gupta, Deepa Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781665473507

Automated correction of common and systematic errors caused by the ASR system is part of the post-processing of automatic speech recognition (ASR). The ASR system's output is prone to grammatical, spelling and phonetic problems. The sentences which are the outputs from Automated Speech Recognition (ASR) models have been refined using error correction approaches to obtain a reduced word error rate (WER) and Character Error Rate (CER) than the initial ASR outputs. In this paper we propose a model that reduces the word error rate and character error rate of the speech recognized mathematical equations. The proposed model is the encoder-decoder with attention model.

关键词： Phonetic errors Automatic Speech Recognition (ASR) Mathematical equations encoder-decoder Attention Word Error Rate (WER) Character Error Rate (CER)

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