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Recurrent Attention and Semantic Gate for Remote Sensing Image Captioning

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Li, Yunpeng Zhang, Xiangrong Gu, Jing Li, Chen Wang, Xin Tang, Xu Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

The remote sensing image captioning has attracted wide spread attention in remote sensing field due to its application potentiality. However, most existing approaches model limited interactions between image content and sentence and fail to exploit special characteristics of the remote sensing images. We introduce a novel recurrent attention and semantic gate (RASG) framework to facilitate the remote sensing image captioning in this article, which integrates competitive visual features and a recurrent attention mechanism to generate a better context vector for the images every time as well as enhances the representations of the current word state. Specifically, we first project each image into competitive visual features by taking the advantage of both static visual features and multiscale features. Then, a novel recurrent attention mechanism is developed to extract the high-level attentive maps from encoded features and nonvisual features, which can help the decoder recognize and focus on the effective information for understanding the complex content of the remote sensing images. Finally, the hidden states from the long short-term memory (LSTM) and other semantic references are incorporated into a semantic gate, which contributes to more comprehensive and precise semantic understanding. Comprehensive experiments on three widely used datasets, Sydney-Captions, UCM-Captions, and Remote Sensing Image Captioning Dataset, have demonstrated the superiority of the proposed RASG over a series of attentive models based on image captioning methods.

关键词： Feature extraction Semantics Visualization Remote sensing Logic gates Decoding Neural networks Attention mechanism encoder-decoder remote sensing image captioning semantic understanding

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A semi-supervised deep learning model for ship encounter situation classification

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OCEAN ENGINEERING 2021年 239卷 109824-109824页

作者： Chen, Xiang Liu, Yuanchang Achuthan, Kamalasudhan Zhang, Xinyu Chen, Jinhai UCL Dept Civil Environm & Geomat Engn Chadwick Bldg London WC1E 6BT England UCL Dept Mech Engn Torrington Pl London WC1E 7JE England Dalian Maritime Univ Key Lab Maritime Dynam Simulat & Control Minist Transportat Dalian 116026 Peoples R China Jimei Univ Nav Coll Nationallocal Joint Engn Res Ctr Marine Nav Aids Serv Xiamen 361021 Peoples R China

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship's operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships' reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships' encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship's AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship's movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi Supervised Convolutional encoder-decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder-decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by o

关键词： Automatic Identification System (AIS) Semi-supervised learning Deep learning Convolutional neural network encoder-decoder Trajectory data Encounter situation classification

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FPA-Net: Frequency-Guided Position-Based Attention Network for Land Cover Image Segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第11期37卷 2354015-2354015页

作者： Rubel, Al Shahriar Shih, Frank Y. New Jersey Inst Technol Dept Informat Newark NJ 07102 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan

Land cover segmentation has been a significant research area because of its multiple applications including the infrastructure development, forestry, agriculture, urban planning, and climate change research. In this paper, we propose a novel segmentation method, called Frequency-guided Position-based Attention Network (FPA-Net), for land cover image segmentation. Our method is based on encoder-decoder improved U-Net architecture with position-based attention mechanism and frequency-guided component. The position-based attention block is used to capture the spatial dependency among different feature maps and obtain the relationship among relevant patterns across the image. The frequency-guided component provides additional support with high-frequency features. Our model is simple and efficient in terms of time and space complexities. Experimental results on the Deep Globe, GID-15, and Land Cover AI datasets show that the proposed FPA-Net can achieve the best performance in both quantitative and qualitative measures as compared against other existing approaches.

关键词： Land cover segmentation encoder-decoder attention mechanism residual connection frequency-guided component U-Net

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Aspect-based sentiment analysis with attention-assisted graph and variational sentence representation

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KNOWLEDGE-BASED SYSTEMS 2022年 258卷

作者： Feng, Shi Wang, Bing Yang, Zhiyao Ouyang, Jihong Jilin Univ Coll Comp Sci & Technol Changchun 130022 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130022 Peoples R China

Aspect-based sentiment analysis (ABSA) is a fine-grained task that detects the sentiment polarities of particular aspect words in a sentence. With the rise of graph convolution networks (GCNs), current ABSA models mostly use graph-based methods. These methods construct a dependency tree for each sentence, and regard each word as a unique node. To be more specific, they conduct classification using aspect representations instead of sentence representations, and update them with GCNs. However, this kind of method relies too much on the quality of the dependency tree and may lose the global sentence information, which is also helpful for classification. To deal with these, we design a new ABSA model AG-VSR. Two kinds of representations are proposed to perform the final classification, Attention-assisted Graph-based Representation (A2GR) and Variational Sentence Representation (VSR). A2GR is produced by the GCN module, which inputs a dependency tree modified by the attention mechanism. Furthermore, VSR is sampled from a distribution learned by a VAE-like encoder-decoder structure. Extensive experiments show that our model AG-VSR achieves competitive results. Our code and data have been released in https://***/wangbing1416/VAGR.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Aspect-based sentiment analysis Graph neural network encoder-decoder Self-attention

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Network traffic prediction with Attention-based Spatial-Temporal Graph Network

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COMPUTER NETWORKS 2024年 243卷

作者： Peng, Yufei Guo, Yingya Hao, Run Xu, Chengzhe Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Beijing Peoples R China

Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention -based Spatial-Temporal Graph Network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.

关键词： Network traffic prediction Attention mechanism Graph neural network Temporal and spatial encoder-decoder

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Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework

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SENSORS 2022年第15期22卷 5861-5861页

作者： Li, Yuxuan Yang, Chunjie Sun, Youxian Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.

关键词： LSTM semi-supervised learning FeO content soft sensor encoder-decoder dynamic feature extraction

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Deep convolutional neural networks for semantic segmentation of cracks

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STRUCTURAL CONTROL & HEALTH MONITORING 2022年第1期29卷 e2850-e2850页

作者： Wang, Jia-Ji Liu, Yu-Fei Nie, Xin Mo, Y. L. Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Univ Houston Dept Civil & Environm Engn Houston TX 77204 USA

A large crack detection dataset of 2446 manually labeled images is established to cover a wide range of noise and to evaluate the performance of end-to-end deep convolutional networks in detecting cracking. Five state-of-the-art end-to-end deep computer vision architectures for semantic segmentation are trained and evaluated, including Fully Convolutional Network (FCN), Global Convolutional Network (GCN), Pyramid Scene Parsing Network (PSPNet), UPerNet, and DeepLabv3+. For the backbones, the VGG, ResNet, and DenseNet are adopted. Based on the comparison of test set metrics, DeepLabv3+ with the ResNet101 backbone achieved the highest IoU of 0.6298, the highest recall of 0.6834, and the highest F1 score of 0.7732. The influence of database choice and image noise on crack detection performance is reported. Based on the comparison of predicted images, UperNet with ResNet101 backbone shows the highest performance for images with shadings, while DeepLabv3+ with ResNet101 backbone shows the best performance for images with blemishes. The research outcome can provide reference for the application of fast and accurate detection of cracks in civil engineering.

关键词： computer vision convolutional neural network crack deep learning encoder-decoder semantic segmentation

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Asymmetric cross-modal activation network for RGB-T salient object detection

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KNOWLEDGE-BASED SYSTEMS 2022年 258卷

作者： Xu, Chang Li, Qingwu Zhou, Qingkai Jiang, Xiongbiao Yu, Dabing Zhou, Yaqin Hohai Univ Coll Internet Things Engn Changzhou 213022 Jiangsu Peoples R China Hohai Univ Jiangsu Key Lab Power Transmiss & Distribut Equipm Changzhou 213022 Jiangsu Peoples R China

RGB-thermal salient object detection (RGB-T SOD) has unique advantages in terms of handling challenging scenes with cluttered backgrounds, low illumination, and low contrast. However, because they do not consider the significant differences between different imaging mechanisms and inherent characteristics of thermal images, existing RGB-T SOD methods are generally unable to handle diverse feature fusion demands and may yield unsatisfactory performance. To overcome this problem and achieve more effective RGB-T SOD, we propose an asymmetric cross-modal activation network to exploit the interactions of modality-specific features based on an asymmetric feature fusion strategy. Specifically, a two-stream asymmetric feature aggregation encoder module is proposed to fuse multimodality features adaptively and extract complementary information. The self-attention of multimodality features is leveraged to guide cross-modal interactions, which can propagate long-range contextual dependencies and extract effective saliency cues. Furthermore, a multitask decoder is proposed to achieve SOD and thermal image reconstruction in a unified framework. Salient objects can be located and segmented accurately based on reconstructed high-resolution feature representations. Extensive experiments on public RGB-T and RGB-D SOD datasets demonstrate the superiority of the proposed network and ablation experiments highlight the effectiveness of each component. Our code and saliency maps are available at: ***/xanxuso/ACMANet.(c) 2022 Elsevier B.V. All rights reserved.

关键词： RGB-T salient object detection Multimodality Feature fusion encoder-decoder Long-range dependence

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Stroke constrained attention network for online handwritten mathematical expression recognition

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PATTERN RECOGNITION 2021年 119卷 108047-108047页

作者： Wang, Jiaming Du, Jun Zhang, Jianshu Wang, Bin Ren, Bo Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

In this paper, we propose a novel stroke constrained attention network (SCAN) which treats stroke as the basic unit for encoder-decoder based online handwritten mathematical expression recognition (HMER). Unlike previous methods which use trace points or image pixels as basic units, SCAN makes full use of stroke-level information for better alignment and representation. The proposed SCAN can be adopted in both single-modal (online or offline) and multi-modal HMER. For single-modal HMER, SCAN first employs a CNN-GRU encoder to extract point-level features from input traces in online mode and employs a CNN encoder to extract pixel-level features from input images in offline mode, then use stroke constrained information to convert them into online and offline stroke-level features. Using stroke-level features can explicitly group points or pixels belonging to the same stroke, therefore reduces the difficulty of symbol segmentation and recognition via the decoder with attention mechanism. For multi-modal HMER, other than fusing multi-modal information in decoder, SCAN can also fuse multi-modal information in encoder by utilizing the stroke based alignments between online and offline modalities. The encoder fusion is a better way for combining multi-modal information as it implements the information interaction one step before the decoder fusion so that the advantages of multiple modalities can be exploited earlier and more adequately. Besides, we propose an approach combining the encoder fusion and decoder fusion, namely encoder-decoder fusion, which can further improve the performance. Evaluated on a benchmark published by CROHME competition, the proposed SCAN achieves the state-of-the-art performance. Furthermore, by conducting experiments on an additional task: online handwritten Chinese character recognition (HCCR), we demonstrate the generality of our proposed method. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Stroke-level information Multi-modal fusion encoder-decoder Attention mechanism Handwritten mathematical expression recognition

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Attention-Based Convolutional LSTM for Describing Video

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IEEE ACCESS 2020年 8卷 133713-133724页

作者： Liu, Zhongyu Chen, Tian Ding, Enjie Liu, Yafeng Yu, Wanli China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China China Univ Min & Technol IOT Percept Mine Res Ctr Xuzhou 221000 Jiangsu Peoples R China Univ Bremen Inst Electrodynam & Microelect D-28359 Bremen Germany

Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the upper layer output of pre-trained convolutional neural networks (CNNs);The decoder uses the recurrent neural networks to generate a textual sentence. However, the upper layers of convolutional neural networks contain low-resolution, semantically strong, while the lower layers contain high-resolution, semantically weak features. In the existing method, the multi-scale information of CNNs is hardly considered to be used in the video description. Ignoring this information will lead to the problem that the video description is not detailed and comprehensive. This paper applies the hierarchical convolutional long short-term memory (ConvLSTM) in the encode-decode framework to conduct feature extraction of the upper and lower layers in CNNs. Moreover, multiple network structures are designed to explore the Spatio-temporal feature extraction performance of ConvLSTM, which can approach the optimal accuracy in the three-layer ConvLSTM. In order to efficiently improve the language quality of video description, the attention mechanism focuses on visual feature output by ConvLSTM. The extensive experimental results demonstrate that the proposed method outperforms the existing approaches.

关键词： Feature extraction Visualization Decoding Task analysis Semantics Recurrent neural networks Convolution Video description long short-term memory encoder-decoder attention mechanism

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