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• W3195416090 abstract "Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. Attention mechanisms have become a very important concept within deep learning approach, their operation tries to imitate the visual capacity of people that allows them to focus their attention on relevant parts of a scene to extract important information. In this paper we propose a soft attention mechanism adapted to a base CNN–LSTM architecture. First, a VGG16 convolutional neural network extracts the features from the input video. Then an LSTM classifies the video into a particular class. To carry out the training and testing phases, we used the HMDB-51 and UCF-101 datasets. We evaluate the performance of our system using accuracy as an evaluation metric, obtaining 40.7% (base approach), 51.2% (with attention) for HMDB-51 and 75.8% (base approach), 87.2% (with attention) for UCF-101." @default.
• W3195416090 created "2021-08-30" @default.
• W3195416090 creator A5051753326 @default.
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• W3195416090 date "2020-12-01" @default.
• W3195416090 modified "2023-10-17" @default.
• W3195416090 title "Towards an Attention Mechanism LSTM Framework for Human Action Recognition in Videos" @default.
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• W3195416090 doi "https://doi.org/10.1109/argencon49523.2020.9505440" @default.
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