Micro-Attention Branch, a Flexible Plug-In That Enhances Existing 3D ConvNets
نویسندگان
چکیده
In the field of video motion recognition, with increase in network depth, there is an asymmetry amount parameters and their accuracy. We propose structure micro-attention branches a method integrating attention multi-branches 3D convolution networks. Our proposed can improve this problem. Attention be flexibly added to form plug-in, without changing overall original network. Through structure, newly constructed fuse features extracted by real time process feature extraction. By adding branches, model focus on action subject more accurately, so as accuracy model. Moreover, case that are multiple sub-branches construction module existing network, well adapt scenario. kinetics dataset, we use our branch construct deep which compared The experimental results show recognition improved 3.6% while trained only increased 0.6%.
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ژورنال
عنوان ژورنال: Symmetry
سال: 2022
ISSN: ['0865-4824', '2226-1877']
DOI: https://doi.org/10.3390/sym14040639