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  • Open Access


    Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention

    Jianming Zhang1,2,*, Kai Wang1,2, Yaoqi He1,2, Lidan Kuang1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 909-927, 2022, DOI:10.32604/cmes.2022.020471

    Abstract Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the features of different layers are enhanced by the coordinate attention block. We then send these features separately into… More >

  • Open Access


    TP-MobNet: A Two-pass Mobile Network for Low-complexity Classification of Acoustic Scene

    Soonshin Seo1, Junseok Oh2, Eunsoo Cho2, Hosung Park2, Gyujin Kim2, Ji-Hwan Kim2,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3291-3303, 2022, DOI:10.32604/cmc.2022.026259

    Abstract Acoustic scene classification (ASC) is a method of recognizing and classifying environments that employ acoustic signals. Various ASC approaches based on deep learning have been developed, with convolutional neural networks (CNNs) proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models. When using ASC systems in the real world, model complexity and device robustness are essential considerations. In this paper, we propose a two-pass mobile network for low-complexity classification of the acoustic scene, named TP-MobNet. With inverse residuals and linear bottlenecks, TP-MobNet is based on MobileNetV2, and following mobile blocks,… More >

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