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Pixel Voting decoder: A novel decoder that regresses pixel relationships for segmentation

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期193卷 116438-116438页

作者： Xian, Pengfei Po, Lai-Man Xiong, Jingjing Zhou, Chang Zhao, Yuzhi Yu, Wing-Yin Ou, Weifeng Zhang, Yujia Zhang, Xiaori City Univ Hong Kong Hong Kong Peoples R China Fudan Univ Shanghai Peoples R China

With the rapid development of the convolutional neural network, both instance segmentation and semantic segmentation have achieved remarkable performances. Recently, many efforts have been made to use a unified encoder-decoder architecture to solve these two segmentation tasks simultaneously. The encoder extracts high-level features from the input images for both tasks. However, existing decoders cannot meet the performance requirements of these two tasks: the semantic segmentation decoder is not flexible enough for instance segmentation, and the instance segmentation decoder lacks the precision of semantic segmentation. Therefore, we introduce a novel Pixel Voting decoder to satisfy both precision and flexibility. The proposed decoder regresses the interlayer pixel relationships between the input and output feature maps across the convolutional layers. Then, the pixel relationships are regarded as the pixel votes for dynamically decoding the higher level information from the encoder. Finally, we propose the dynamic deconvolution to make full use of the votes for each pixel during the decoding process. Meanwhile, the matrix computation for the dynamic deconvolution is designed to boost the calculation. Experiments show that the proposed method can achieve better performance than the well-known methods on both instance segmentation on the COCO dataset and semantic segmentation on the Cityscapes dataset. The matrix implementation of the dynamic deconvolution also shows its high efficiency and feasibility.

关键词： Convolutional neural network Dynamic deconvolution encoder-decoder Image segmentation Pixel voting Residual block

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An attention based dual learning approach for video captioning

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APPLIED SOFT COMPUTING 2022年第0期117卷 108332-108332页

作者： Ji, Wanting Wang, Ruili Tian, Yan Wang, Xun Liaoning Univ Sch Informat Shenyang Peoples R China Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Hangzhou Peoples R China

Video captioning aims to generate sentences/captions to describe video contents. It is one of the key tasks in the field of multimedia processing. However, most of the current video captioning approaches utilize only the visual information of a video to generate captions. Recently, a new encoder-decoderreconstructor architecture was developed for video captioning, which can capture the information in both raw videos and the generated captions through dual learning. Based on this architecture, this paper proposes a novel attention based dual learning approach (ADL) for video captioning. Specifically, ADL is composed of a caption generation module and a video reconstruction module. The caption generation module builds a translatable mapping between raw video frames and the generated video captions, i.e., using the visual features extracted from videos by an Inception-V4 network to produce video captions. Then the video reconstruction module reproduces raw video frames using the generated video captions, i.e., using the hidden states of the decoder in the caption generation module to reproduce/synthesize raw visual features. A multi-head attention mechanism is adopted to help the two modules focus on the most effective information in videos and captions, and a dual learning mechanism is adopted to fine-tune the performance of the two modules to generate final video captions. Therefore, ADL can minimize the semantic gap between raw videos and the generated captions by minimizing the differences between the reproduced and the raw videos, thereby improving the quality of the generated video captions. Experimental results demonstrate that ADL is superior to the state-of-the-art video captioning approaches on benchmark datasets. (C) 2021 Published by Elsevier B.V.

关键词： Attention mechanism Deep neural network Dual learning encoder-decoder Video captioning

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基于深度学习的多模态融合情感分类模型

基于深度学习的多模态融合情感分类模型

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作者：周洁大连理工大学

学位级别：硕士

多模态情感分析是指对包含多种模态的信息载体中的情感进行分析的过程。随着互联网的发展,信息越来越趋向于多模态化,相比较于单一模态只能描述事物的某一个方面,这种包含多种模态的数据呈现的内容更丰富,更多元化。本文通过对视频数据... 详细信息

多模态情感分析是指对包含多种模态的信息载体中的情感进行分析的过程。随着互联网的发展,信息越来越趋向于多模态化,相比较于单一模态只能描述事物的某一个方面,这种包含多种模态的数据呈现的内容更丰富,更多元化。本文通过对视频数据进行情感分析,考虑文本、音频和视觉三种模态信息,能更加准确地理解人类的情感表达,减少由单一模态信息造成的歧义或者误解,具有深远的应用价值。本文针对多模态融合方法进行了研究调查,并结合了最新的自然语言处理相关技术,提出了一种基于encoder-decoder的多模态融合模型,该模型通过不同的编码函数分别捕捉模态内交互特征和模态间交互特征,最后再通过Transformer解码层输出模态融合特征进行预测。根据在三个公开数据集上的对比实验分析,本文提出的多模态融合模型能够有效地改进多模态情感分析任务的性能,并通过消融实验以及模态分布可视化,直观地验证了该模型方法的有效性和可行性。在基于文本情感信息为主导的多模态情感分析方法的研究上,本文提出了基于BERT的跨模态融合情感分析模型。该模型主要考虑以文本模态为主的跨模态融合特征,引入跨模态注意力机制,编码文本和音频、文本和视觉的跨模态动态交互信息,对预先训练的BERT模型进行微调,以此提高情感预测的性能。在多模态情感分析数据集上进行对比实验,实验结果表明,本文提出的模型具有较好的性能表现,并对各个模态组件进行消融实验,再将注意力权重进行可视化,从不同维度上展示模型结构的作用。

关键词：多模态情感分析深度学习 BERT 跨模态融合 encoder-decoder

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Reconstructing QRS Complex From PPG by Transformed Attentional Neural Networks

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IEEE SENSORS JOURNAL 2020年第20期20卷 12374-12383页

作者： Chiu, Hong-Yu Shuai, Hong-Han Chao, Paul C. -P. Chiao Tung Univ NCTU BASIC Lab Dept Elect & Comp Engn Hsinchu 30010 Taiwan Natl Chiao Tung Univ NCTU Dept Elect & Comp Engn Sensors IC Lab Hsinchu 30010 Taiwan

Technology that translates photoplethysmogram (PPG) into the QRS complex of electrocardiogram (ECG) would be transformative for people who require continuously monitoring. However, directly decoding the QRS complex of ECG from PPG is challenging because PPG signals usually have different offsets due to 1) different devices, and 2) personal differences, which makes the alignment difficult. In this paper, we make the first attempt to reconstruct the QRS complex of ECG only from the recording of PPG by an end-to-end deep learning-based approach. Specifically, we propose a novel encoder-decoder architecture containing three components: 1) a sequence transformer network which automatically calibrates the offset, 2) an attention network, which dynamically identifies regions of interest, and 3) a new QRS complex-enhanced loss for better reconstruction. The experiment results on a real dataset demonstrate the effectiveness of the proposed method: 3.67% R peak failure rate of the reconstructed ECG and high correlation of pulse transit time between the reconstructed QRS complex and the groundtruth QRS complex (rho = 0.844), which creates a new opportunity for low-cost clinical studies via the waveform-level reconstruction of the QRS complex of ECG from PPG.

关键词： Electrocardiography Monitoring Electrodes Biomedical monitoring Skin Standards Sensors Convolutional neural network electrocardiography encoder-decoder photoplethysmography transform network

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Tracking Performance Limitations of MIMO Networked Control Systems With Multiple Communication Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第7期50卷 2982-2995页

作者： Chen, Chao-Yang Gui, Weihua Wu, Lianghong Liu, Zhaohua Yan, Huaicheng Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Boston Univ Ctr Polymer Studies Boston MA 02215 USA Boston Univ Dept Phys Boston MA 02215 USA Cent South Univ Sch Informat Sci & Engn Changsha 410012 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Hubei Normal Univ Coll Mechatron & Control Engn Huangshi 435002 Hubei Peoples R China

In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs are considered as continuous-time linear multi-input multioutput (MIMO) systems with random reference noises. The controlled plants include unstable poles and nonminimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree-of-freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises [the reference noise power distribution (RNPD) and its directions], and the characteristics of communication constraints. The characteristics of communication constraints include communication noise power distribution (CNPD);quantization noise power distribution (QNPD), and their distribution directions;transform bandwidth allocation (TBA);transform encoder-decoder allocation (TEA), and their allocation directions;and NMP zeros and MP part of bandwidth. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) can always affect the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channel. Finally, an instance is given for verifying the effectiveness of the theoretical outcomes.

关键词： MIMO communication Control systems Bandwidth Poles and zeros Power distribution Resource management Linear systems Bandwidth communication noise encoder-decoder performance limitation power distribution quantization noise reference noise

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Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics

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ENERGY 2020年第0期195卷 116964-000页

作者： Wang, Shaomin Wang, Shouxiang Chen, Haiwen Gu, Qiang Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Xianghe Elect Co Ltd Tianjin 300072 Peoples R China State Grid Tianjin Elect Power Res Inst Tianjin 300022 Peoples R China

Accurate multi-energy load forecasting (MELF) is the key to realize the balance between supply and demand in regional integrated energy systems (RIES). To this end, a hybrid MELF method for RIES considering temporal dynamic and coupling characteristics (MELF_TDCC) is proposed. The novelty of MELF_TDCC lies in the following three aspects: 1) considering the high-dimensional temporal dynamic characteristic, an encoder-decoder model based on long-short term memory network (LSTMED) is proposed, which can extract the high dimensional potential feature, and reflect the temporal dynamic characteristics of historical load sequence effectively;2) considering the cross-coupling characteristic, a coupling feature matrix of multi-energy load is constructed, which reflects the cross-influence of electricity, cooling and heating loads;3) with the feature fusion layer of the hybrid model being built by gradient boosting decision tree (GBDT), the extended feature matrix for each class of load is constructed considering the intra-class inherent characteristics and inter-class coupling characteristic of loads, and the GBDT model is trained on the extended feature matrix, which provides multi-dimensional perspective for researching load essential characteristics. MELF_TDCC is verified on the ultra-short-term and short-term MELF scenarios based on an actual dataset. The simulation result shows that the proposed MELF_TDCC outperforms the current advanced methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： encoder-decoder Long short-term memory Multi-energy load forecasting Regional integrated energy systems Rolling forecasting

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Large-scale 3D point-cloud semantic segmentation of urban and rural scenes using data volume decomposition coupled with pipeline parallelism

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AUTOMATION IN CONSTRUCTION 2022年第0期133卷 103995-103995页

作者： Chew, Alvin Wei Ze Ji, Ankang Zhang, Limao Bentley Syst Res Off 1 Harbourfront PlHarbourFront Tower One Singapore 098633 Singapore Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

This study proposes a generic approach which performs a series of systematic analyses by first introducing a data volume decomposition method to generate useful data features for performing semantic segmentation analysis involving 3D point-cloud data. Pipeline parallelism protocol is then implemented to accelerate the deep learning model's training phase. Our proposed approach is verified by decomposing around 2.0 billion point-cloud data points, as extracted from an open-source Semantic3D dataset, into many 3D regular structures with defined numbers of voxels. Each derived 3D structure has imposed normality in their data distribution of the respective label classes. Using the optimal hyperparameters for model training, the resulting trained model achieves average overall accuracy (mOA) and average intersection over union (mIOU) values of 0.984 and 0.752, respectively, on a testing dataset having close to 800 million point-cloud data points. The results are comparable with that of other state-of-the-art models in the literature.

关键词： Deep learning Image segmentation analysis encoder-decoder Pipeline parallelism 3D point-cloud data

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Model-Based Underwater Image Simulation and Learning-Based Underwater Image Enhancement Method

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INFORMATION 2022年第4期13卷 187-187页

作者： Liu, Yidan Xu, Huiping Zhang, Bing Sun, Kelin Yang, Jingchuan Li, Bo Li, Chen Quan, Xiangqian Chinese Acad Sci Inst Deep Sea Sci & Engn Sanya 572000 Peoples R China

Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation and learning-based underwater image enhancement method for coping with various degradation problems in underwater images. We first derive a simplified model for describing various degradation problems in underwater images, then propose a model-based image simulation method that can generate images with a wide range of parameter values. The proposed image simulation method also comes with an image-selection part, which helps to prune the simulation dataset so that it can serve as a training set for learning to enhance the targeted underwater images. Afterwards, we propose a convolutional neural network based on the encoder-decoder backbone to learn to enhance various underwater images from the simulated images. Experiments on simulated and real underwater images with different degradation problems demonstrate the effectiveness of the proposed underwater image simulation and enhancement method, and reveal the advantages of the proposed method in comparison with many state-of-the-art methods.

关键词： underwater image simulation underwater image enhancement modeling of underwater image degradation encoder-decoder deep learning

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Very-Short-Term Probabilistic Forecasting for a Risk-Aware Participation in the Single Price Imbalance Settlement

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第2期35卷 1218-1230页

作者： Bottieau, Jeremie Hubert, Louis De Greve, Zacharie Vallee, Francois Toubeau, Jean-Francois Univ Mons Elect Power Engn Unit B-7000 Mons Belgium

The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bi-level optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources.

关键词： Deep learning electricity markets encoder-decoder robust optimization single imbalance pricing

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Uneven Image Dehazing by Heterogeneous Twin Network

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IEEE ACCESS 2020年 8卷 118485-118496页

作者： Wang, Keping Yang, Yi Li, Bingfeng Li, Xinwei Cui, Lizhi Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China

Removing the haze in an image is a huge challenge due to the difficulty of accurate hazy image modeling. Although the atmospheric scattering model (ASM) is widely used to describe the formation of hazy images, it is hard to deal with the uneven haze image, once the ASM is restricted with the assumption that the atmosphere distributed homogeneously. This paper analyzes the haze imaging mechanism, then proposes an image-to-image architecture to handle the uneven image dehazing, in which a heterogeneous twin network (HT-Net) with two parallel sub-networks are constructed to establish the high dimensional nonlinear mapping model between the hazy and clean images. Consequently, the inhomogeneous haze is removed by the symmetric U-shape network with encoder-decoder structure, meanwhile, the other enhancement network extracts the high-frequency feature from the hazy image to compensate the edge and texture of the object. The effectiveness is validated by the experiments based on three real haze image datasets which depict the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. One professional uneven haze image dataset is found in the real environment and covers 190 types of scene and 21975 uneven images accordingly. This dataset includes thin, heavy, and uneven haze images. The other two benchmark datasets are I-HAZE and O-HAZE, respectively including 35 pairs of indoor real haze and haze-free (ground-truth) images and 45 different outdoor scenes. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze and achieve superior performance over the other mentioned methods.

关键词： Uneven image dehazing restoration atmospheric scattering model (ASM) deep learning encoder-decoder

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