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Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
[article]
2018
arXiv
pre-print
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved significantly. However, to date, conventional research has only explored relatively shallow 3D architectures. We examine the architectures of various 3D CNNs from relatively shallow to very deep ones
arXiv:1711.09577v2
fatcat:vdo7vkhspjfkbe2w5ojho5zj4e