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Split, Embed and Merge: An accurate table structure recognizer

PATTERN RECOGNITION 2022年 126卷 108565-108565页

作者： Zhang, Zhenrong Zhang, Jianshu Du, Jun Wang, Fengren Univ Sci & Technol China Natl Engn Res Ctr Speech & Language Informat Proc 96 JinZhai Rd Hefei Anhui Peoples R China IFLYTEK Res Hefei Anhui Peoples R China

Table structure recognition is an essential part for making machines understand tables. Its main task is to recognize the internal structure of a table. However, due to the complexity and diversity in their structure and style, it is very difficult to parse the tabular data into the structured format which machines can understand, especially for complex tables. In this paper, we introduce Split, Embed and Merge (SEM), an accurate table structure recognizer. SEM is mainly composed of three parts, splitter, embedder and merger. In the first stage, we apply the splitter to predict the potential regions of the table row/column separators, and obtain the fine grid structure of the table. In the second stage, by taking a full consideration of the textual information in the table, we fuse the output features for each table grid from both vision and text modalities. Moreover, we achieve a higher precision in our experiments through providing additional textual features. Finally, we process the merging of these basic table grids in a self-regression manner. The corresponding merging results are learned through the attention mechanism. In our experiments, SEM achieves an average F1-Measure of 97 . 11% on the SciTSR dataset which outperforms other methods by a large margin. We also won the first place of complex tables and third place of all tables in Task-B of ICDAR 2021 Competition on Scientific Literature Parsing. Extensive experiments on other publicly available datasets further demonstrate the effectiveness of our proposed approach. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Table structure recognition Self-regression Attention mechanism encoder-decoder

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Improved Short-term Dense Bottleneck network for efficient scene analysis

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COMPUTER VISION AND IMAGE UNDERSTANDING 2023年第1期235卷

作者： Singha, Tanmay Pham, Duc-Son Krishna, Aneesh Curtin Univ Perth WA 6102 Australia

Visual scene understanding mainly depends on pixel-wise classification obtained from a deep convolutional neural network. However, existing semantic segmentation models often face difficulties in real-time appli-cations due to their large network architecture. Although there are real-time semantic segmentation models available, their shallow backbone can degrade the performance considerably. This paper introduces SDBNetV2, a lightweight semantic segmentation model designed to improve real-time performance without increasing computational costs. A key contribution is a novel Short-term Dense Bottleneck (SDB) module in the encoder, which provides varied field-of-views to capture different geometrical objects in a complex scene. Additionally, we propose dense feature refinement and improved semantic aggregation modules at the decoder end to enhance contextualization and object localization. We evaluate the proposed model's performance on several indoor and outdoor datasets in structured and unstructured environments. The results show that SDBNetV2 achieves superior segmentation performance over other real-time models with less than 2 million parameters.

关键词： Semantic segmentation Convolutional neural network Scene understanding Real-time Machine learning encoder-decoder

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Automatic concrete crack segmentation model based on transformer

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AUTOMATION IN CONSTRUCTION 2022年 139卷

作者： Wang, Wenjun Su, Chao Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Routine visual inspection of concrete structures is essential to maintain safe conditions. Therefore, studies of concrete crack segmentation using deep learning methods have been extensively conducted in recent years. However, insufficient performance remains a major challenge in diverse field-inspection scenarios. In this study, a novel SegCrack model for pixel-level crack segmentation is therefore proposed using a hierarchically structured Transformer encoder to output multiscale features and a top-down pathway with lateral connections to progressively up-sample and fuse features from the deepest layer of the encoder. Furthermore, an online hard example mining strategy was adopted to strengthen the detection of hard samples and improve the model performance. The effect of dataset size on the segmentation performance was then investigated. The results indicated that SegCrack achieved a precision, recall, F1 score, and mean intersection over union of 96.66%, 95.46%, 96.05%, and 92.63%, respectively, using the test set.

关键词： Concrete crack Pixel-wise segmentation Visual transformer Self-attention encoder-decoder

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Non-intrusive residential electricity load decomposition via low-resource model transferring

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JOURNAL OF BUILDING ENGINEERING 2023年 73卷

作者： Lin, Lin Shi, Jiancheng Ma, Changwen Zuo, Shi Zhang, Jie Chen, Cheng Huang, Nantian Jilin Inst Chem Technol Coll Informat & Control Engn Jilin 132022 Peoples R China Shanghai Second Polytech Univ Sch Environm & Mat Engn Shanghai 201209 Peoples R China State Grid Jilin Elect Power Co Ltd Econ & Tech Res Inst Changchun 130022 Peoples R China Northeast Dianli Univ Sch Elect Engn Jilin 132012 Peoples R China

Non-intrusive load decomposition (NILD) technology has a broad application prospect because it can deeply excavate the internal electricity consumption data of customers and obtain electricity consumption information. A non-intrusive load decomposition method based on model transfer is proposed to address the problem of insufficient accumulation of historical electricity consumption data of some household customers. Firstly, Time2vec, an embedding layer with time vectors, is introduced as an encoder to convert the time series into a continuous vector space, which improves the computational efficiency of the model for power time series information. Then, by introducing the Channel-wise attention mechanism, favorable residential electricity load characteristics can be effectively obtained. The model transfer method is used to fine-tune the pretrained model based on complete data to overcome the problem of insufficient accumulation of historical electricity consumption data of some households. The experimental results show that the proposed method is a significant improvement over the existing state-of-the-art methods. The proposed method significantly improves the accuracy of load decomposition while reducing the model parameters. The model parameters are reduced by 62.4%, and the average absolute error is reduced by 13.8%-59.3%. Even with a small number of data training source domains, the proposed method is still able to accurately obtain the power consumption information of each appliance, proving that the proposed method overcomes the problem of insufficient accumulation of electricity consumption data of household users.

关键词： Residential electricity load Non-intrusive load decomposition encoder-decoder Channel-wise attention mechanism Model transferring network

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Attention-based Gate Recurrent Unit for remaining useful life prediction in prognostics

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APPLIED SOFT COMPUTING 2023年 143卷

作者： Lin, Ruiguan Wang, Huawei Xiong, Minglan Hou, Zhaoguo Che, Changchang Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China

An essential process in prognostics and health management (PHM) is remaining useful life (RUL) prediction. The traditional Recurrent Neural Networks (RNNs) and their variants are not very efficient at solving the regression problems of RUL prediction. Given this problem, an attention-based Gate Recurrent Unit (ABGRU) for RUL prediction is proposed in this paper. Firstly, the dataset is preprocessed, and the RUL labels are modeled using the piecewise linear degradation method. Then, a GRU network based on an encoder-decoder framework with an attention mechanism is proposed. The network can assign weights according to the importance of feature information and effectively use the feature information to predict RUL. The validity of the proposed framework is verified in the NASA C-MAPSS benchmark dataset. The results show that the presented method outperforms the existing state-of-the-art approaches and provides a new solution for RUL Prediction. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Prognostics Attention Gate Recurrent Unit Remaining useful life prediction encoder-decoder long short-term memory (LSTM) [18 19]

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MUMC: Minimizing uncertainty of mixture of cues

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IMAGE AND VISION COMPUTING 2021年 115卷 104280-104280页

作者： Patro, Badri N. Kurmi, Vinod K. Kumar, Sandeep Namboodiri, Vinay P. Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Dept Comp Sci & Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Dept Math & Stat Kanpur Uttar Pradesh India

Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here, we provide project link for Deep Bayesian VQG: https://***/BVQG/. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Uncertainty estimation Mixture of cues Visual Question Answering Paraphrase Visual Question Generation LSTM CNN encoder-decoder

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PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 389卷 114399-114399页

作者： Ren, Pu Rao, Chengping Liu, Yang Wang, Jian-Xun Sun, Hao Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing 100872 Peoples R China MIT Dept Civil & Environm Engn 77 Massachusetts Ave Cambridge MA 02139 USA

Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep learning have shown the great potential of physics-informed neural networks (PINNs) to solve PDEs as a basis for data-driven modeling and inverse analysis. However, the majority of existing PINN methods, based on fully-connected NNs, pose intrinsic limitations to low-dimensional spatiotemporal parameterizations. Moreover, since the initial/boundary conditions (I/BCs) are softly imposed via penalty, the solution quality heavily relies on hyperparameter tuning. To this end, we propose the novel physics-informed convolutional-recurrent learning architectures (PhyCRNet and PhyCRNet-s) for solving PDEs without any labeled data. Specifically, an encoder-decoder convolutional long short-term memory network is proposed for low-dimensional spatial feature extraction and temporal evolution learning. The loss function is defined as the aggregated discretized PDE residuals, while the I/BCs are hard-encoded in the network to ensure forcible satisfaction (e.g., periodic boundary padding). The networks are further enhanced by autoregressive and residual connections that explicitly simulate time marching. The performance of our proposed methods has been assessed by solving three nonlinear PDEs (e.g., 2D Burgers' equations, the lambda-omega and FitzHugh Nagumo reaction-diffusion equations), and compared against the start-of-the-art baseline algorithms. The numerical results demonstrate the superiority of our proposed methodology in the context of solution accuracy, extrapolability and generalizability. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Convolutional-recurrent learning Partial differential equations encoder-decoder Physics-informed deep learning Residual connection Hard-encoding of I/BCs

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A Dual-Attention-Based Stock Price Trend Prediction Model With Dual Features

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IEEE ACCESS 2019年 7卷 148047-148058页

作者： Chen, Yingxuan Lin, Weiwei Wang, James Z. South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Clemson Univ Sch Comp Clemson SC 29631 USA Guangdong Luan Ind & Commerce Co Ltd Guangzhou 510520 Peoples R China

Modeling and predicting stock prices is an important and challenging task in the field of financial market. Due to the high volatility of stock prices, traditional data mining methods cannot identify the most relevant and critical market data for predicting stock price trend. This paper proposes a stock price trend predictive model (TPM) based on an encoder-decoder framework that predicting the stock price movement and its duration adaptively. This model consists of two phases, first, a dual feature extraction method based on different time spans is proposed to get more information from the market data. While traditional methods only extract features from information at some specific time points, this proposed model applies the PLR method and CNN to extract the long-term temporal features and the short-term spatial features from market data. Then, in the second phase of the proposed TPM, a dual attention mechanism based encoder-decoder framework is used to select and merge relevant dual features and predict the stock price trend. To evaluate our proposed TPM, we collected high-frequency market data for stock indexes CSI300, SSE 50 and CSI 500, and conducted experiments based on these three data sets. The experimental results show that the proposed TPM outperforms the existing state-of-art methods, including SVR, LSTM, CNN, LSTM_CNN and TPM_NC, in terms of prediction accuracy.

关键词： Stock price predictive models neural networks encoder-decoder dual feature extraction dual attention mechanism

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Lightweight semantic segmentation network with configurable context and small object attention

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2023年 17卷 1280640页

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Li, Jinzhao Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China Univ Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Beijing Peoples R China

The current semantic segmentation algorithms suffer from encoding feature distortion and small object feature loss. Context information exchange can effectively address the feature distortion problem, but it has the issue of fixed spatial range. Maintaining the input feature resolution can reduce the loss of small object information but would slow down the network's operation speed. To tackle these problems, we propose a lightweight semantic segmentation network with configurable context and small object attention (CCSONet). CCSONet includes a long-short distance configurable context feature enhancement module (LSCFEM) and a small object attention decoding module (SOADM). The LSCFEM differs from the regular context exchange module by configuring long and short-range relevant features for the current feature, providing a broader and more flexible spatial range. The SOADM enhances the features of small objects by establishing correlations among objects of the same category, avoiding the introduction of redundancy issues caused by high-resolution features. On the Cityscapes and Camvid datasets, our network achieves the accuracy of 76.9 mIoU and 73.1 mIoU, respectively, while maintaining speeds of 87 FPS and 138 FPS. It outperforms other lightweight semantic segmentation algorithms in terms of accuracy.

关键词： semantic segmentation lightweight network context feature enhancement small object attention encoder-decoder

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Linear Contrast Enhancement Network for Low-Illumination Image Enhancement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhou, Zhaorun Shi, Zhenghao Ren, Wenqi Xian Univ Technol Dept Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China

Images captured under low-illumination conditions usually suffer from severe degradations, such as fading and low contrast, drastically affecting the performance of systems relying on images under low-illumination conditions. To address such problems, this study proposes a linear contrast enhancement network (LCENet) for low-illumination image enhancement. It consists of three subnets: two encoder-decoder-based subnets for gradient map restoration and brightness enhancement, respectively, and a backbone network for adaptive brightness and contrast adjustment. In addition, a linear contrast enhancement adaptive instance normalization (LCEAIN) module with linear contrast enhancement ability is proposed in the backbone network, which can avoid the problem of ignoring contrast enhancement when enhancing image brightness. Considerable evaluations on both synthetic and real low-illumination images show that the proposed method performs favorably against other existing similar methods. Moreover, our method can handle complex low-illuminance conditions and has good generalization for low-illuminance scenes with backlighting, night scenes with light sources, as well as underwater scenes with low illuminance. Code: https://***/zhouzhaorun/LCENet.

关键词： Brightness Image restoration Lighting Image enhancement Deep learning Image color analysis Task analysis Convolutional neural network encoder-decoder linear contrast enhancement low-illumination image enhancement

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