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An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds

AUTOMATION IN CONSTRUCTION 2022年 137卷 104187-104187页

作者： Ji, Ankang Chew, Alvin Wei Ze Xue, Xiaolong Zhang, Limao Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Bentley Syst Res Off 1 Harbourfront PlHarbourFront Tower One Singapore 098633 Singapore Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Discovering seepage is widely thought to be critical for maintaining the healthy conditions of the tunnel. Unfortunately, most of the seepage surveys are still manual with tedious, time-consuming, and inefficient as well as work-related physical injuries. To address this problem, this research proposes an encoder-decoder deep learning method combined with point cloud techniques for multi-class object segmentation, including seepage, from 3D tunnel point clouds. This method develops data processing and feature extraction techniques to perform normalization of 3D point clouds with full consideration of point features, followed by constructing voxels as input to the proposed encoder-decoder architecture for learning. In the training process, an optimal model is selected with a learning rate of 0.0001, a batch size of 256, and a voxel boundary of 8. Subsequently, the optimal well-trained model is applied to the testing set, achieving excellent performance. Comparisons with other stateof-the-art methods and four data processing strategies are conducted, demonstrating that the proposed method outperforms in segmenting large-scale 3D point clouds. Overall, the proposed method performs excellently, beneficially contributing to the multi-class object segmentation from 3D tunnel point clouds with great practical potential.

关键词： Deep learning encoder-decoder Multi-class object segmentation 3D tunnel point-clouds Segmentation

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A novel encoder-decoder structure for Time Series analysis based on Bayesian Uncertainty reduction

A novel Encoder-Decoder structure for Time Series analysis b...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Llugsi, Ricardo El Yacoubi, Samira Fontaine, Allyx Lupera, Pablo Natl Polytech Sch Dept Elect Telecommun & Informat Networks DETRI Quito Ecuador Univ Perpignan Via Domitia UMR Espace Dev Perpignan France Univ Guyane UMR Espace Dev Cayenne French Guiana

ISBN: (纸本)9781728188645

In the present work, a novel Convolutional LSTM encoder-decoder structure for the implementation of Weather Forecast for the Andean city of Quito is presented. Aside from the above, the encoder-decoder structure uses a Walk-Forward validation, an adjustment of the Bayesian posterior predictive distribution and the ADAMW optimizer to carry out the forecast. The aforementioned stages are combined to obtain 4 error metrics per hour. The prediction is done in base of acquired data from a network of Automatic Weather Stations. The results show that the Convolutional encoder-decoder structure with a dropout probability of 0.05 and a model precision equal to 0.1 performs better than a LSTM model, LSTM Stacked model or ARIMA models reaching a maximum error of 1.03 degrees C. Finally, the methodology could be applied as an effective option to implement the post-processing stage for the physical model of a Weather Forecast System.

关键词： LSTM Time Series Convolutional encoder-decoder Neural Network Walk-Forward validation Dropout Bayesian uncertainty

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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...

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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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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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Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure

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JOURNAL OF HYDROLOGY 2022年 609卷

作者： Cui, Zhen Zhou, Yanlai Guo, Shenglian Wang, Jun Xu, Chong-Yu Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Univ Oslo Dept Geosci POB 1047 Blindern N-0316 Oslo Norway

Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The encoder-decoder (ED) that can tackle sequence-to-sequence problems is suitable for multistep-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTMEDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model.

关键词： Flood forecasting Multi-step-ahead Neural network Deep learning encoder-decoder Exogenous input

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CUFD: An encoder-decoder network for visible and infrared image fusion based on common and unique feature decomposition

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COMPUTER VISION AND IMAGE UNDERSTANDING 2022年第0期218卷

作者： Xu, Han Gong, Meiqi Tian, Xin Huang, Jun Ma, Jiayi Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China

In this paper, we propose a novel method for visible and infrared image fusion by decomposing feature information, which is termed as CUFD. It adopts two pairs of encoder-decoder networks to implement feature map extraction and decomposition, respectively. On the one hand, the shallow features of the image contain abundant information while the deep features focus more on extracting the thermal targets. Thus, we use an encoder-decoder network to extract both shallow and deep features. Unlike existing methods, both of the shallow and deep features are used for fusion and reconstruction with different emphases. On the other hand, the infrared and visible features of the same layer have both similarities and differences. Therefore, we train the other encoder-decoder network to decompose the feature maps into common and unique information based on their similarities and differences. After that, we apply different fusion rules according to the flexible requirements. This operation is more beneficial to retain the significant feature information in the fusion results. Qualitative and quantitative experiments on publicly available TNO and RoadScene datasets demonstrate the superiority of our CUFD over the state-of-the-art.

关键词： Image fusion Infrared Visible Feature map encoder-decoder

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Pedestrian Trajectory Prediction in Heterogeneous Traffic using Facial Keypoints-based Convolutional encoder-decoder Network

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2022年第4期22卷 83-83页

作者： Xiao, Song Chen, Kai Ren, Xiaoxiang Yuan, Haitao Beihang Univ Beijing Peoples R China Nanan Jr High Sch Quanzhou Peoples R China New Jersey Inst Technol Newark NJ 07102 USA

Future pedestrian trajectory prediction offers great prospects for many practical applications such as unmanned vehicles, building evacuation design and robotic path planning. Most existing methods focus on social interaction among pedestrians but ignore the fact that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her facial keypoints. Considering this, this work proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her facial keypoints. To fulfill this, an end-to-end facial keypoints-based convolutional encoder-decoder network (FK-CEN) is designed, in which the heterogeneous traffic and facial keypoints are input. After training, FK-CEN is evaluated on 5 crowded video sequences collected from the public datasets MOT-16 and MOT-17. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.

关键词： Social-interaction pedestrian intention convolutional long-short-term memory encoder-decoder attention

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TED-Net: Convolution-Free T2T Vision Transformer-Based encoder-decoder Dilation Network for Low-Dose CT Denoising 12th

TED-Net: Convolution-Free T2T Vision Transformer-Based Encod...

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12th International Workshop on Machine Learning in Medical Imaging (MLMI 2021)

作者： Wang, Dayang Wu, Zhan Yu, Hengyong Univ Massachusetts Lowell Dept Elect & Comp Engn Lowell MA 01854 USA

ISBN: (纸本)9783030875886;9783030875893

Low dose computed tomography (CT) is a mainstream for clinical applications. However, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more artifacts which are obstacles for practical applications. In the last few years, convolution-based end-to-end deep learning methods have been widely used for LDCT image denoising. Recently, transformer has shown superior performance over convolution with more feature interactions. Yet its applications in LDCT denoising have not been fully cultivated. Here, we propose a convolution-free T2T vision transformer-based encoderdecoder Dilation Network (TED-Net) to enrich the family of LDCT denoising algorithms. The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer. Our model (Codes are available at https://***/wdayang/TED- Net) is evaluated on the AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance over the state-of-the-art denoising methods.

关键词： Low-dose CT Transformer Token-to-Token encoder-decoder Dilation

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Micro-climate Prediction - Multi Scale encoder-decoder based Deep Learning Framework 21

Micro-climate Prediction - Multi Scale Encoder-decoder based...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Kumar, Peeyush Chandra, Ranveer Bansal, Chetan Kalyanaraman, Shivkumar Ganu, Tanuja Grant, Michael Microsoft Res Redmond WA 98052 USA Microsoft Bengaluru India Microsoft Res Bengaluru India Univ Washington Seattle WA 98195 USA Climate Corp San Francisco CA USA

ISBN: (纸本)9781450383325

This paper presents a deep learning approach for a versatile Microclimate prediction framework (DeepMC). Micro climate predictions are of critical importance across various applications, such as Agriculture, Forestry, Energy, Search & Rescue, etc. To the best of our knowledge, there is no other single framework which can accurately predict various micro-climate entities using Internet of Things (IoT) data. We present a generic framework (DeepMC) which predicts various climatic parameters such as soil moisture, humidity, wind speed, radiation, temperature based on the requirement over a period of 12 hours - 120 hours with a varying resolution of 1 hour - 6 hours, respectively. This framework proposes the following new ideas: 1) Localization of weather forecast to IoT sensors by fusing weather station forecasts with the decomposition of IoT data at multiple scales and 2) A multi-scale encoder and two levels of attention mechanisms which learns a latent representation of the interaction between various resolutions of the IoT sensor data and weather station forecasts. We present multiple real-world agricultural and energy scenarios, and report results with uncertainty estimates from the live deployment of DeepMC, which demonstrate that DeepMC outperforms various baseline methods and reports 90%+ accuracy with tight error bounds.

关键词： Micro-climate prediction Sustainability Deep learning encoder-decoder Attention mechanism Transfer learning IoT sensors Sequence to sequence Time series Agriculture Energy Wavelet transform

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