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  • Open Access


    Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

    Guoqing Zhou, Liang Huang, Qiao Sun*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1985-2007, 2023, DOI:10.32604/cmc.2023.040902

    Abstract The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote… More >

  • Open Access


    Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

    Wenkai Qin1, Tianliang Lu1,*, Lu Zhang2, Shufan Peng1, Da Wan1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417

    Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details… More >

  • Open Access


    A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

    Yang Zhang1, Liru Qiu2, Yongkai Zhu1, Long Wen1,*, Xiaoping Luo2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 873-894, 2022, DOI:10.32604/cmes.2022.022322

    Abstract Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4… More >

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