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Exploring Data and Models in SAR Ship Image Captioning

IEEE ACCESS 2022年 10卷 91150-91159页

作者： Zhao, Kai Xiong, Wei Space Engn Univ Sci & Technol Complex Elect Syst Simulat Lab Beijing 101400 Peoples R China

In recent years, considerable progress has been made in ship detection in synthetic aperture radar (SAR) images;however, no research has been conducted on translating SAR ship images into flexible and accurate sentences. To explore image captions in SAR ship images, we conduct the following work: first, to better describe SAR ship images, we propose certain principles for SAR image annotation based on the characteristics of SAR images. Second, to make better use of SAR ship images, a large-scale SAR ship image captioning dataset is carefully constructed. Finally, we explore encoder-decoder models and the attention mechanism and apply these methods to the SAR ship image captioning task. We conduct detailed experiments on the proposed dataset and find that the encoder-decoder model and attention mechanism can obtain good results in the SAR ship image captioning task. The experiments also reveal that the generated sentences can accurately describe SAR ship images. This dataset has already been published on https://***/5132210/***.

关键词： Marine vehicles Synthetic aperture radar Radar polarimetry Remote sensing Seaports Feature extraction Decoding Encoding Recurrent neural networks SAR image image captioning encoder-decoder recurrent neural network long short-term memory network

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On the role of the architecture for spring discharge prediction with deep learning approaches

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HYDROLOGICAL PROCESSES 2022年第10期36卷 e14737-e14737页

作者： Zhou, Renjie Zhang, Yanyan Sam Houston State Univ Dept Environm & Geosci Huntsville TX 77340 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77840 USA

Understanding karst spring flow is important to accommodate the increasing water demand caused by the population growth and manage the freshwater water resource effectively. However, due to the spatial and temporal heterogeneity and complex hydrological processes in karst systems, predicting karst spring discharge remains challenging. In this study, three deep learning-based models, including long short-term memory (LSTM), gated recurrent unit (GRU) and simple recurrent neural network (RNN), are framed with an encoder-decoder architecture to provide multiple-step-ahead spring discharge prediction. The encoder-decoder architecture includes an encoder that reads and encodes the input sequence into a vector and decoder that deciphers the vector and outputs the predicted sequence. Three hybrid models called LSTM-ED, GRU-ED and simple RNN-ED are compared with single-step models and multiple-step models without the encoder-decoder architecture to investigate the role of the encoder-decoder architecture on multi-step-ahead prediction. The sensitivity of the selection of input time and lead time steps on the karst spring discharge prediction is evaluated. The predicted results are compared with the observed spring discharge. It implies that: (1) LSTM-ED, GRU-ED and RNN-ED models obtain similar results on predicting karst spring discharge multiple time steps ahead;(2) three hybrid multiple-step models outperform the single-step models in making consistent and accurate spring discharge predictions;(3) the multiple-step models framed with an encoder-decoder architecture obtain better spring discharge prediction results than the single-step models and multiple-step models without the encoder-decoder structure;(4) the LSTM-ED, GRU-ED and simple RNN-ED models are sensitive to the selection of lead time and insensitive to the selection of input time step. A short lead time typically yields a more accurate spring discharge prediction.

关键词： encoder-decoder GRU karst LSTM RNN sequence-to-sequence spring discharge

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FMAM-Net: Fusion Multi-Scale Attention Mechanism Network for Building Segmentation in Remote Sensing Images

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IEEE ACCESS 2022年 10卷 134241-134251页

作者： Ye, Huanran Zhou, Run Wang, Jianhao Huang, Zhiliang Yiwu Ind & Commercial Coll Sch Mech & Elect Informat Jinhua 322000 Zhejiang Peoples R China Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Zhejiang Peoples R China

As the largest target in remote sensing images, buildings have important application value in urban planning and old city reconstruction. However, most networks have poor recognition ability on high resolution images, resulting in blurred boundaries in the segmented building maps. Then, the similarity between buildings and backgrounds will lead to inter-class indistinction. Finally, the diversity of buildings brings difficulties to segmentation, which requires the network to have better generalization ability. To address these problems, we propose Fusion Multi-scale Attention Mechanism Network (FMAM-Net). Firstly, we design Feature Refine Compensation Module(FRCM) to improve the boundary ambiguity problem, including Feature Refinement Module(FRM) and Feature Compensation Module(FCM). FRM utilizes the densely connected architecture to refine features and increase recognition capabilities. FCM introduces low-level features to make up for the lack of boundary information in high-level features. Secondly, to handle inter-class indistinction, we design Tandem Attention Module(TAM) and Parallel Attention Module(PAM). TAM is designed to sequentially filter some features from channels and spaces for adaptive feature refinement. PAM combines context information and uses high-level features to guide low-level features to select more distinguishable features. Finally, based on the binary cross entropy loss function, we add an evaluation index to reduce the error caused by determining the optimization direction only through cross entropy. On the Inria Aerial Image Labeling Dataset, FMAM-Net achieves mean IoU of 85.34%, which is 5.58% higher than AMUNet and 3.77% higher than our baseline(U-Net ResNet-34). On the WHU Dataset, IoU reached the maximum value of 91.06% on FMAM-Net, 1.67% higher than SARB-UNet and 0.2% higher than MAP-Net. The visualization results show that FMAM-Net improves the fuzzy boundary of building segmentation and reduces the inter-class indistinction.

关键词： Remote sensing image building segmentation attention mechanism feature refinement encoder-decoder

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R-GRU: Regularized gated recurrent unit for handwritten mathematical expression recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第22期81卷 31405-31419页

作者： Pal, Aniket Singh, Krishna Pratap Indian Inst Informat Technol Dept Informat Technol Machine Learning & Optimizat Lab Allahabad Prayagraj India

Handwritten mathematical expression recognition(HMER) plays a wide variety of roles in different domains like online teaching, scientific research, etc. Due to its two-dimensional non-linear structure, it is a challenging problem. In this work, we proposed a novel architecture using regularization(dropout), attention & gated recurrent unit(R-GRU). R-GRU is used as the central decoding unit that takes the intermediate representation produced by the encoder as an input and generates corresponding LaTeX sequences. Simulation on CROHME 2014 and 2016 datasets achieve results comparable to the latest state-of-the-art.

关键词： Handwritten Mathematical Expression Recognition encoder-decoder Gated recurrent unit Dropout Regularization

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A deep multivariate time series multistep forecasting network

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APPLIED INTELLIGENCE 2022年第8期52卷 8956-8974页

作者： Yin, Chenrui Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

Due to that multivariate time series, multistep forecasting technology has a guiding role in many fields, such as electricity consumption, traffic flow detection, and stock price prediction, many approaches have been proposed, seeking to realize accurate prediction based on historical data. However, multivariate time series in real-world applications often contain complex and non-linear interdependencies between time steps and series, for which traditional approaches that just model dependencies in time dimension may fail to make forecasting accurately. In addition, the traditional cyclic multistep prediction strategy leads to error accumulation and consequently results in the reduction of prediction performance. To address these limitations, we propose a novel Deep Multivariate Time Series Multistep Forecasting Network based on the encoder-decoder model, abbreviated as DualMNet. DualMNet uses the temporal patterns module to capture long-term patterns between time steps, and, simultaneously, employs the spatial patterns module to discover the interdependencies between time series. Furthermore, the decoder utilizes the historical data of the target sequence to predict the long time-series sequences at the final forward operation, significantly ameliorating the issue of error accumulation. Finally, the conducted t-test results, based upon the extensive experimental results on the three benchmark multivariate time series datasets, they demonstrate that the multistep predictive performance of DualMNet is significantly superior to those of the comparison models, and the ablation study shows that integrating all the components of DualMNet together results in robust forecasting performance.

关键词： Multivariate time series Multistep prediction Deep learning encoder-decoder Attention mechanism

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Video summarization with a dual-path attentive network

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NEUROCOMPUTING 2022年 467卷 1-9页

作者： Liang, Guoqiang Lv, Yanbing Li, Shucheng Wang, Xiahong Zhang, Yanning Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian Peoples R China

With the explosive growth of videos captured everyday, how to efficiently extract useful information from videos has become a more and more important problem. As one of the most effective methods, video summarization aiming to extract the most important frames or shots has attracted more interests recently. Currently, lots of methods employ a recurrent structure. However, due to its step-by-step char-acteristic, it is difficult to parallelize these models. To address this problem, we propose a dual-path attentive video summarization framework consisting of a temporal spatial encoder, a score-aware enco-der and a decoder. And all of them are mainly based on multi-head self-attention and convolutional block attention module. The temporal spatial encoder is to capture the temporal and spatial information while the score-aware encoder incorporates the appearance features with previously predicted frame-level importance scores. By combining the scores and appearance features, our model can better capture the long-range global dependencies and update the importance scores of previous frames continuously. Moreover, entirely based on attention mechanism, our model can be trained in full parallel, which leads to less training time. To validate the method, we employ the two popular datasets SumMe and TVSum. The experimental results show the effectiveness of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Video summarization Attention mechanism encoder-decoder

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DCNet: A Deformable Convolutional Cloud Detection Network for Remote Sensing Imagery

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Liu, Yang Wang, Wen Li, Qingyong Min, Min Yao, Zhigang Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Zhuhai 519082 Peoples R China Beijing Inst Appl Meteorol Beijing 100029 Peoples R China

Recently, deep convolutional neural networks (CNNs) have made important progress in cloud detection with powerful representation learning capability and yield significant performance. However, most existing CNN-based cloud detection methods still face serious challenges because of the variable geometry of clouds and the complexity of underlying surfaces. It is attributed that they only use the fixed grid to extract contextual information, which lacks internal mechanisms to handle the geometric transformations of clouds. To tackle this problem, we propose a deformable convolutional cloud detection network with an encoder-decoder architecture, named DCNet, which can enhance the adaptability of a model to cloud variations. Specifically, we introduce deformable convolution blocks at the encoder to capture saliency spatial contexts adaptively based on the morphological characteristics of clouds and generate high-level semantic representations. After this, we incorporate skip-connection mechanisms into the decoder that integrate low-level spatial contexts as guidance to recover high-level semantic pixel localization and export precise cloud-detection results. Extensive experiments on the GF-1 wide field-of-view (WFV) Satellite Imagery demonstrate that DCNet outperforms several state-of-the-art methods. A public reference implementation of our proposed model in PyTorch is available at shapehttps://***/NiAn-creator/***.

关键词： Clouds Convolution Feature extraction Semantics Satellites Decoding Task analysis Cloud detection deformable convolution block (DCB) encoder-decoder semantic segmentation

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Dynamic Attention Filter Capsule Network for Medical Images Segmentation 19th

Dynamic Attention Filter Capsule Network for Medical Images ...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Chen, Ran Hu, Kai Zhao, Zhong-Qiu Hefei Univ Technol Coll Comp & Informat Hefei Peoples R China HFUT Intelligent Mfg Inst Hefei Peoples R China Hefei Univ Technol Intelligent Interconnected Syst Lab Anhui Prov Hefei Peoples R China Guangxi Acad Sci Nanning Guangxi Peoples R China

ISBN: (纸本)9789819947416;9789819947423

In the existing works, Capsule Networks (CapsNets) have been proven to be promising alternatives to Convolutional Neural Networks (CNNs). However, CapsNets perform poorly on complex datasets with RGB backgrounds and cannot handle images with large input sizes. We propose a Dynamic Attention Filter (DAF) method to improve the performance of CapsNets. DAF is a filter unit between low-level capsule layers and voting layers, which can effectively filter the invalid background capsules and improve the classification performance of CapsNet. Besides, we propose DAF-CapsUNet for inputting medical images of large sizes, combining the advantages of the U-shaped encoder-decoder structure and DAF-CapsNet. Specifically, it contains three fundamental operations: an encoder is responsible for extracting shallow feature information, a capsule module is responsible for capturing the detailed feature information lost due to the pooling layer of the CNNs, and a decoder is responsible for fusing the feature information extracted from the two stages. Extensive experiments demonstrate that DAF can improve the performance of CapsNets on complex datasets and reduce the number of parameters, GPU memory cost, and running time of CapsNets. Benefiting from DAF-CapsNet, our model can achieve more useful information for precise localization. The medical experiments show that DAF-CapsUNet achieves SOTA performance compared to other segmentation models.

关键词： CapsNets DAF encoder-decoder DAF-CapsUNet

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Relative Position Embedding Asymmetric Siamese Network for Offline Handwritten Mathematical Expression recognition 17th

Relative Position Embedding Asymmetric Siamese Network for O...

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

作者： Wang, Chunyi Luo, Hurunqi Rao, Xiaqing Hui, Wei Bi, Ning Tan, Jun Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China Meituan Shanghai Peoples R China

ISBN: (纸本)9783031416750;9783031416767

Currently, Recurrent Neural Network(RNN)-based encoder-decoder models are widely used in handwritten mathematical expression recognition (HMER). Due to its recursive pattern, the problem of gradient disappearance or gradient explosion also exists for RNN, which makes them inefficient in processing long HME sequences. In order to solve above problems, this paper proposes a Transformer-based encoder-decoder model consisting of an asymmetric siamese network, relative position embedding Transformer (ASNRT). With the assistance of printed images, the asymmetric siamese network further narrows the difference betweeen feature maps of similar formula images and increases the encoding gap between dissimilar formula images. We insert coordinate attention into the encoder, additionally we replace RNN with Transformer as the decoder. Moreover, rotary position embedding is used, incorporating relative position information through absolute embedding ways. Given the symmetry of MEs, we adopt the bidirectional decoding strategy. Extensive experiments show that our model improves the ExpRate of state-of-the-art methods on CROHME 2014, CROHME 2016, and CROHME 2019 by 0.94%, 2.18% and 2.12%, respectively.

关键词： Handwritten Contrastive Learning encoder-decoder

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An Efficient Embedding Framework for Uncertain Attribute Graph 34th

An Efficient Embedding Framework for Uncertain Attribute Gra...

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34th International Conference on Database and Expert Systems Applications (DEXA)

作者： Jiang, Ting Yu, Ting Qiao, Xueting Zhang, Ji Zhejiang Lab Hangzhou Peoples R China Univ Southern Queensland Toowoomba Qld Australia

ISBN: (纸本)9783031398209;9783031398216

Graph data with uncertain connections between entities is commonly represented using uncertain graphs. This paper tackles the challenge of graph embedding within such uncertain attribute graphs. Current graph embedding techniques are typically oriented towards deterministic graphs, or uncertain graphs that lack attribute data. Furthermore, the majority of studies on uncertain graph learning simply adapt conventional algorithms for deterministic graphs to handle uncertainty, leading to compromised computational efficiency. To address these issues, we introduce an optimized embedding framework UAGE for uncertain attribute graphs. In UAGE, nodes are represented within a Gaussian distribution space to learn node attributes. We also propose a Probability Similarity Value (PSV) to manage relationship uncertainty and ensure that nodes with higher-order similar structures are located more closely in the latent space. Real-world dataset experiments confirm that UAGE surpasses contemporary methods in performance for downstream tasks.

关键词： Uncertain graph Gaussian embedding encoder-decoder

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