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• W4386187975 abstract "Hand gesture recognition from images is a long-standing computer vision task that can be used to build a potential bridge for human-computer interaction and sign language translation. For number of methods proposed for hand gesture recognition (HGR); however, difficult scenarios such as different scales of hand gestures and complex backgrounds exist, making them less effective. In this paper, we propose an end-to-end multiscale feature learning network for HGR, which consists of a CNN-based backbone network, a feature aggregation pyramid network (FAPN) embedded with a two-stage expansion-squeeze-aggregation (ESA) module, and three task-specific prediction branches. First, the backbone network extracts multiscale features from the original hand gesture images. Furthermore, the FAPN embedded with two-stage ESA extensively exploits multiscale feature information and learns hand gesture-specific features at different scales. Then, the mask loss guides the network to locate hand-specific regions during the training stage, and finally, the classification and regression branches output the category and location of a hand gesture during the model training and prediction. The experimental results on two publicly available datasets show that the proposed method outperforms most state-of-the-art HGR methods." @default.
• W4386187975 created "2023-08-27" @default.
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• W4386187975 date "2024-01-01" @default.
• W4386187975 modified "2023-10-16" @default.
• W4386187975 title "Mask-Guided Multiscale Feature Aggregation Network for Hand Gesture Recognition" @default.
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• W4386187975 doi "https://doi.org/10.1016/j.patcog.2023.109901" @default.
• W4386187975 hasPublicationYear "2024" @default.
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