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  • Open Access


    Criss-Cross Attention Based Auto Encoder for Video Anomaly Event Detection

    Jiaqi Wang1, Jie Zhang2, Genlin Ji2,*, Bo Sheng3

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1629-1642, 2022, DOI:10.32604/iasc.2022.029535

    Abstract The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change… More >

  • Open Access


    HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks

    Yu Xue*, Ziming Yuan

    Journal on Internet of Things, Vol.4, No.1, pp. 57-67, 2022, DOI:10.32604/jiot.2022.025327

    Abstract The attention mechanism is one of the most important priori knowledge to enhance convolutional neural networks. Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input. This is a popular attention strategy design method. Global contextual information helps the network to consider the overall distribution, while local contextual information is more general. The contextual information makes the network pay attention to the mean or maximum value of a particular receptive field. Different from the most attention mechanism, this article proposes a novel attention mechanism with… More >

  • Open Access


    Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

    Yin Liang1,*, Gaoxu Xu1, Sadaqat ur Rehman2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4645-4661, 2022, DOI:10.32604/cmc.2022.026999

    Abstract Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC… More >

  • Open Access


    Visual Saliency Prediction Using Attention-based Cross-modal Integration Network in RGB-D Images

    Xinyue Zhang1, Ting Jin1,*, Mingjie Han1, Jingsheng Lei2, Zhichao Cao3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 439-452, 2021, DOI:10.32604/iasc.2021.018643

    Abstract Saliency prediction has recently gained a large number of attention for the sake of the rapid development of deep neural networks in computer vision tasks. However, there are still dilemmas that need to be addressed. In this paper, we design a visual saliency prediction model using attention-based cross-model integration strategies in RGB-D images. Unlike other symmetric feature extraction networks, we exploit asymmetric networks to effectively extract depth features as the complementary information of RGB information. Then we propose attention modules to integrate cross-modal feature information and emphasize the feature representation of salient regions, meanwhile neglect… More >

  • Open Access


    AttEF: Convolutional LSTM Encoder-Forecaster with Attention Module for Precipitation Nowcasting

    Wei Fang1,2,*, Lin Pang1, Weinan Yi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 453-466, 2021, DOI:10.32604/iasc.2021.016589

    Abstract Precipitation nowcasting has become an essential technology underlying various public services ranging from weather advisories to citywide rainfall alerts. The main challenge facing many algorithms is the high non-linearity and temporal-spatial complexity of the radar image. Convolutional Long Short-Term Memory (ConvLSTM) is appropriate for modeling spatiotemporal variations as it integrates the convolution operator into recurrent state transition functions. However, the technical characteristic of encoding the input sequence into a fixed-size vector cannot guarantee that ConvLSTM maintains adequate sequence representations in the information flow, which affects the performance of the task. In this paper, we propose… More >

  • Open Access


    Adaptive Multi-Scale HyperNet with Bi-Direction Residual Attention Module for Scene Text Detection

    Junjie Qu, Jin Liu*, Chao Yu

    Journal of Information Hiding and Privacy Protection, Vol.3, No.2, pp. 83-89, 2021, DOI:10.32604/jihpp.2021.017181

    Abstract Scene text detection is an important step in the scene text reading system. There are still two problems during the existing text detection methods: (1) The small receptive of the convolutional layer in text detection is not sufficiently sensitive to the target area in the image; (2) The deep receptive of the convolutional layer in text detection lose a lot of spatial feature information. Therefore, detecting scene text remains a challenging issue. In this work, we design an effective text detector named Adaptive Multi-Scale HyperNet (AMSHN) to improve texts detection performance. Specifically, AMSHN enhances the More >

  • Open Access


    ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module

    Yudong Zhang1,3,*, Xin Zhang2,*, Weiguo Zhu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1037-1058, 2021, DOI:10.32604/cmes.2021.015807

    Abstract Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network for COVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed to avoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structure of which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracy of our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: This proposed ANC method is superior to 9 state-of-the-art approaches. More >

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