Using efficient group pseudo-3D network to learn spatio-temporal features

Action classification is a challenging problem in computer vision in recent years; the three-dimensional convolutional neural network plays an important role in spatio-temporal feature extraction. However, the 3D convolution approach needs expensive computation and memory resources. This paper proposes an efficient group pseudo-3D (GP3D) convolution to reduce the model size and need less computational power. We built the GP3D with MobileNetV3 to extend the 2D pre-training parameters directly to the 3D convolutional network. We also used GP3D to replace the original inflated 3D convolutional network to efficiently reduce the model size. Compared with other state-of-the-art 3D convolutional networks, GP3D with the efficient network of MobileNetV3 can save about 3 to 22 times of parameters but maintain the same accuracy on the dataset of UCF-101. GP3D with an inflated 3D convolutional network can achieve about 90% top1 accuracy, while the model size is only about half of the original inflated 3D convolutional network.

Automated summary

A study introduces an efficient group pseudo-3D (GP3D) convolution approach to reduce model size and computational power for action classification; By combining GP3D with MobileNetV3, the method saves parameters and maintains accuracy on the UCF-101 dataset; Compared to the inflated 3D convolutional network, GP3D achieves higher accuracy with significantly reduced model size.