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  • Open Access

    ARTICLE

    ACSF-ED: Adaptive Cross-Scale Fusion Encoder-Decoder for Spatio-Temporal Action Detection

    Wenju Wang1, Zehua Gu1,*, Bang Tang1, Sen Wang2, Jianfei Hao2

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2389-2414, 2025, DOI:10.32604/cmc.2024.057392 - 17 February 2025

    Abstract Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A More >

  • Open Access

    ARTICLE

    Pancreas Segmentation Optimization Based on Coarse-to-Fine Scheme

    Xu Yao1,2, Chengjian Qiu1, Yuqing Song1, Zhe Liu1,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2583-2594, 2023, DOI:10.32604/iasc.2023.037205 - 11 September 2023

    Abstract As the pancreas only occupies a small region in the whole abdominal computed tomography (CT) scans and has high variability in shape, location and size, deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background. To alleviate these issues, this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure, in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box, and the fine is for improving the accuracy of pancreas segmentation More >

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