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Image captioning in Hindi language using transformer networks

COMPUTERS & ELECTRICAL ENGINEERING 2021年 92卷 107114-107114页

作者： Mishra, Santosh Kumar Dhir, Rijul Saha, Sriparna Bhattacharyya, Pushpak Singh, Amit Kumar Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India

Neural encoder-decoder architectures have been used extensively for image captioning. Con-volutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are popularly used in encoder and decoder models. Recurrent Neural Networks are popular architectures in natural language processing used for language modeling, but they are sequential in nature. The transformer model can solve this problem of sequential dependency by using an attention mechanism. Many works are available for image captioning in the English language, but models for generating Hindi captions are limited;hence, we have tried to fill this gap. We have created the Hindi dataset for image captioning by manually translating the popular MSCOCO dataset from English to Hindi. Experimental results show that our proposed model outperforms other models. The proposed model has attained the BLEU-1 score of 62.9, BLEU-2 score of 43.3, BLEU-3 score of 29.1, and BLEU4 score of 19.0.

关键词： Image captioning Transformer model Deep learning encoder-decoder Hindi language

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Attention-Based Multistage Fusion Network for Remote Sensing Image Pansharpening

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Zhang, Wanwan Li, Jinjiang Hua, Zhen Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China ICT YANTAD Inst Network Technol Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China

Pansharpening is a significant branch in the field of remote sensing image processing, the goal of which is to fuse panchromatic (PAN) and multispectral (MS) images through certain rules to generate high-resolution MS (HRMS) images. Therefore, how to improve the spatial and spectral resolutions of the fused image is the problem that we need to solve urgently. In this article, a multistage remote sensing image fusion network (MRFNet) is proposed on the basis of in-depth research and exploration on the fusion of the PAN and MS images to obtain a clear fused image that can reflect the ground features more comprehensively and completely. The proposed network consists of three stages that are connected by cross-stage fusion. The first two stages are used to extract the features of the PAN and MS images. The structure of the encoder-decoder and the channel attention module are used to extract the features of the remote sensing image in the channel domain. The third stage is the image reconstruction stage fusing the extracted features with the original image to improve the spatial and spectral resolutions of the fused result. A series of experiments are conducted on the benchmark datasets WorldView II, GF-2, and QuickBird. Qualitative analysis and quantitative comparison show the superiority of MRFNet in visual effects and the values of evaluation indicators.

关键词： Pansharpening Feature extraction Spatial resolution Remote sensing Image resolution Sensors Image reconstruction Attention encoder-decoder multispectral (MS) images panchromatic (PAN) images pansharpening spatial resolution spectral resolution

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OSLPNet: A neural network model for street lamp post extraction from street view imagery

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Zhang, Tengda Dai, Jiguang Song, Weidong Zhao, Ruishan Zhang, Bing Liaoning Tech Univ Sch Geomat Yulong St Fuxin 123000 Liaoning Provin Peoples R China Liaoning Tech Univ Inst Spatiotemporal Transportat Data Fuxin Peoples R China

Quickly and accurately obtaining street lamp post information has great application value in smart city construction and automatic vehicle navigation. However, the existing deep learning methods are affected by factors such as the perspective effect, different objects with the same spectrum, and occlusion. There can also be some problems in the semantic segmentation results for street lamp posts, such as under-segmentation, misextraction, and discontinuity. In this paper, we present the OSLPNet model for the extraction of street lamp posts from street view imagery. According to the characteristics of the various scales of street lamp posts in the imagery, a multiscale phased controller (MPC) with multi-level receptive fields is proposed to reduce the under-segmentation problem for street lamp posts. According to the unique "elbow" structure of street lamp posts, deformable convolution is introduced to reduce the problem of misextraction of street lamp posts. According to the topological relationship of street lamp post context, a lightweight spatial context (LSC) module is proposed to solve the problem of discontinuous detection of street lamp posts caused by occlusion. We also proposed two street lamp pole datasets, and experimental results showed that our F1 values can reach 85.2% and 82.4% under both datasets, which is superior to the existing state of art method. The code and datasets are publicly available at htt ps://***/ZzzTD/OSLPNet.

关键词： Convolutional neural network encoder-decoder Street lamp post extraction Engineering application Urban component acquisition

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A lightweight multi-dimension dynamic convolutional network for real-time semantic segmentation

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 1075520页

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Xu, Chenglong Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

Semantic segmentation can address the perceived needs of autonomous driving and micro-robots and is one of the challenging tasks in computer vision. From the application point of view, the difficulty faced by semantic segmentation is how to satisfy inference speed, network parameters, and segmentation accuracy at the same time. This paper proposes a lightweight multi-dimensional dynamic convolutional network (LMDCNet) for real-time semantic segmentation to address this problem. At the core of our architecture is Multidimensional Dynamic Convolution (MDy-Conv), which uses an attention mechanism and factorial convolution to remain efficient while maintaining remarkable accuracy. Specifically, LMDCNet belongs to an asymmetric network architecture. Therefore, we design an encoder module containing MDy-Conv convolution: MS-DAB. The success of this module is attributed to the use of MDy-Conv convolution, which increases the utilization of local and contextual information of features. Furthermore, we design a decoder module containing a feature pyramid and attention: SC-FP, which performs a multi-scale fusion of features accompanied by feature selection. On the Cityscapes and CamVid datasets, LMDCNet achieves accuracies of 73.8 mIoU and 69.6 mIoU at 71.2 FPS and 92.4 FPS, respectively, without pre-training or post-processing. Our designed LMDCNet is trained and inferred only on one 1080Ti GPU. Our experiments show that LMDCNet achieves a good balance between segmentation accuracy and network parameters with only 1.05 M.

关键词： semantic segmentation lightweight network dynamic convolution encoder-decoder multi-dimension convolution

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Diagnostic Model of Coronary Microvascular Disease Combined With Full Convolution Deep Network With Balanced Cross-Entropy Cost Function

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IEEE ACCESS 2019年 7卷 177997-178006页

作者： Pan, Shiwen Zhang, Wei Zhang, Wanjun Xu, Liang Fan, Guohua Gong, Jianping Zhang, Bo Gu, Haibo Soochow Univ Affiliated Hosp 2 Suzhou 215000 Peoples R China

This paper addressed the vessel segmentation and disease diagnostic in coronary angiography image and proposed an encoder-decoder architecture of deep learning with End-to-End model, where encoder is based on ResNet, and the deep features are exacted automatically, and the decoder produces the segmentation result by balanced cross-entropy cost function. Furthermore, batch normalization is employed to decrease the gradient vanishing in the training process, so as to reduce the difficulty of training the deep neural network. The experiment results show that the algorithm effectively exacts the feature and edge information, therefore the complex background disturbance is suppressed convincingly, and the vessel segmentation precision is improved effectively, the segmentation precision for three typical vessels are 0.8365, 0.8924 and 0.6297 respectively;and the F-measure are 0.8514, 0.8786 and 0.7298, respectively. In addition, the experiment results show that our proposed can be generalized to the angiography image within limits.

关键词： Coronary microvascular cross-entropy cost function encoder-decoder deep learning batch normalization

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Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2023年 618卷

作者： Sun, Xiaoyong Chen, Fenghao Wang, Yuchen Lin, Xuefen Ma, Weifeng Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China

Accurate traffic flow prediction is critical for enhancing traffic network operational efficiency. With the continuous expansion of traffic networks, providing reliable and efficient multi-step traffic flow prediction for large-scale traffic networks with a large number of sensors deployed has become a challenging issue. In this paper, we propose a multi-step many-to-many traffic prediction model for large-scale traffic networks, called spatio-temporal Shared GRU (STSGRU), which receive inputs from multiple sensors and provides predictions for all sensors simultaneously. First, we model the weekly pattern of traffic flow, using periodicity to explore long-term temporal features and provide smooth traffic flow to reduce the impact of data volatility. Second, different from existing models, we propose a shared weight mechanism to achieve many-to-many prediction without mapping traffic networks to images or graph structures. The proposed model strikes a delicate balance between complexity and accuracy. We validate the effectiveness of the proposed method on the Caltrans Performance Measurement System (PeMS) dataset. The results show that our model achieves similar prediction performance with advanced graph neural networks and has higher flexibility. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Traffic flow prediction Gated recurrent unit encoder-decoder Periodicity Shared weight

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Multistep short-term wind speed forecasting using transformer

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ENERGY 2022年第PartA期261卷

作者： Wu, Huijuan Meng, Keqilao Fan, Daoerji Zhang, Zhanqiang Liu, Qing Inner Mongolia Univ Technol Coll Energy & Power Engn Aimin St 49 Hohhot 010051 Peoples R China Minist Educ Key Lab Wind Energy & Solar Energy Technol Aimin St 49 Hohhot 010051 Peoples R China Inner Mongolia Autonomous Reg Wind Power Technol & Aimin St 49 Hohhot 010051 Peoples R China Inner Mongolia Univ Coll Elect Informat Engn Coll Rd 235 Hohhot 010021 Peoples R China

Wind power can effectively alleviate the energy crisis. However, its integration into the grid affects power quality and power grid stability. Accurate wind speed prediction is a key factor in the efficient use of wind power. Because of its intermittent and nonstationary nature, wind speed forecasting is difficult, and is the topic of much research, especially long-time multistep forecasts. In this paper, the multistep wind speed prediction problem is regarded as a sequence-to-sequence mapping problem, and a multistep wind speed prediction model based on a transformer is proposed. This model is based on an encoder-decoder architecture, where the encoder generates representations of historical wind speed sequences of any length, the decoder generates arbitrarily long future wind speed sequences, and the encoder and decoder are associated by an attention mechanism. At the same time, the encoder and decoder of Transformer are completely based on a multi-head attention mechanism. For easy modeling, a 1-dimensional original wind speed sequence is transformed to a 16-dimensional sequence by ensemble empirical mode decomposition (EEMD), and the multidimensional wind speed data are directly modeled with Transformer. We trained the model with very large-scale (19 years of data) wind speed data averaged at 10-minute intervals, and performed the evaluation over one-year wind speed data. Results show that our one-step forecast model achieved an average mean absolute error (MAE) and root mean square error (RMSE) of 0.167 and 0.221, respectively. To the best of our knowledge, our 3-, 6-, 12-, and 24-hour multistep forecast model achieves a new state of the art in wind speed forecasting, with respective MAEs of 0.243, 0.290, 0.362, and 0.453, and RMSEs of 0.326, 0.401, 0.513, and 0.651. It is believed that performance can be further improved with better model parameter optimization.

关键词： Wind speed forecasting Transformer EEMD Multistep encoder-decoder

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Devising single in-out long short-term memory univariate models for predicting the electricity price on the day-ahead markets

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CONNECTION SCIENCE 2024年第1期36卷

作者： Bara, Adela Oprea, Simona Vasilica Bucharest Univ Econ Studies Dept Econ Informat & Cybernet Bucharest Romania

We investigate the performance of intelligent systems such as various Long Short-Term Memory (LSTM) and hybrid models to forecast the electricity spot prices considering univariate and multivariate models. Six models are created to handle the Electricity Price Forecast (EPF). Furthermore, an EPF methodology that consists of a LSTM univariate model, namely Single in-out (Sio) model is proposed. It builds on the Day-Ahead electricity Market (DAM) specificity and, as a novelty, it inserts the predicted value back into the sliding input vector to predict the next values until the entire vector of 24 prices is predicted. The proposed model is further enhanced by the convolutional reading of input data that is embedded into the LSTM cell or by a hybrid combination of LSTM and Convolutional Neural Networks (CNN) that interprets sub-sequences of input data and extracts features that are provided as a sequence to the LSTM model. The methodology is validated using data sets from the Romanian Market Operator (OPCOM) and other market operators from Serbia (SEEPEX), Hungary (HUPX) and Bulgaria (IBEX). Our models improve the results for the day-ahead forecast in comparison with other models by 21.02% in terms of Mean Absolute Error (MAE).

关键词： Univariate and multivariate input encoder-decoder convolutional neural networks long short-term memory electricity price day-ahead forecast

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MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

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IEEE ACCESS 2021年 9卷 148384-148408页

作者： Ahmad, Parvez Jin, Hai Alroobaea, Roobaea Qamar, Saqib Zheng, Ran Alnajjar, Fady Aboudi, Fathia Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan 430074 Peoples R China Taif Univ Coll Comp & Informat Technol Dept Comp Sci At Taif 21944 Saudi Arabia Madanapalle Inst Technol & Sci Dept Comp Applicat Madanapalle 517325 Andhra Pradesh India United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates Univ Tunis El Manar High Inst Med Technol Tunisia ISTMT LRBTM Res Lab Biophys & Med Technol Tunis 1006 Tunisia

UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 Magnetic Resonance Imaging (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 Computed Tomographic Perfusion (CTP) testing dataset. MH UNet is available at https://***/parvezamu/MHUnet.

关键词： Image segmentation Biomedical imaging Feature extraction Training Decoding Three-dimensional displays Convolutional neural networks BraTS convolutions dense connections encoder-decoder ISLES MICCAI UNet

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Generating Instructive Questions from Multiple Articles to Guide Reading in E-Bibliotherapy

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SENSORS 2021年第9期21卷 3223页

作者： Xin, Yunxing Cao, Lei Wang, Xin He, Xiaohao Feng, Ling Tsinghua Univ Res Inst Data Sci Dept Comp Sci & Technol Ctr Computat Mental Healthcare Beijing 100084 Peoples R China

E-Bibliotherapy deals with adolescent psychological stress by manually or automatically recommending multiple reading articles around their stressful events, using electronic devices as a medium. To make E-Bibliotherapy really useful, generating instructive questions before their reading is an important step. Such a question shall (a) attract teens' attention;(b) convey the essential message of the reading materials so as to improve teens' active comprehension;and most importantly (c) highlight teens' stress to enable them to generate emotional resonance and thus willingness to pursue the reading. Therefore in this paper, we propose to generate instructive questions from the multiple recommended articles to guide teens to read. Four solutions based on the neural encoder-decoder model are presented to tackle the task. For model training and testing, we construct a novel large-scale QA dataset named TeenQA, which is specific to adolescent stress. Due to the extensibility of question expressions, we incorporate three groups of automatic evaluation metrics as well as one group of human evaluation metrics to examine the quality of the generated questions. The experimental results show that the proposed encoder-decoder with Summary on Contexts with Feature-rich embeddings (ED-SoCF) solution can generate good questions for guiding reading, achieving comparable performance on some semantic similarity metrics with that of humans.

关键词： E-bibliotherapy instructive question reading guidance encoder-decoder dataset

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