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Nov, 2020
自适应时间特征分辨率的3D卷积神经网络
3D CNNs with Adaptive Temporal Feature Resolutions
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Mohsen Fayyaz, Emad Bahrami Rad, Ali Diba, Mehdi Noroozi, Ehsan Adeli...
TL;DR
通过引入可微分的相似性引导采样(SGS)模块,将其作为额外层集成到3D CNN中,可以将3D CNN转换为具有自适应时间特征分辨率(ATFR)的更高效CNN,减少了一半的计算成本(GFLOPs),同时保留甚至提高精度。
Abstract
While state-of-the-art
3d convolutional neural networks
(CNN) achieve very good results on
action recognition
datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CN
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