Spatiotemporal Initialization for 3D CNNs with Generated Motion Patterns

The paper proposes a framework of Formula-Driven Supervised Learning (FDSL) for spatiotemporal initialization. Our FDSL approach enables to automatically and simultaneously generate motion patterns and their video labels with a simple formula which is based on Perlin noise. We designed a dataset of generated motion patterns adequate for the 3D CNNs to learn a better basis set of natural videos. The constructed Video Perlin Noise (VPN) dataset can be applied to initialize a model before pre-training with large-scale video datasets such as Kinetics-400/700, to enhance target task performance. Our spatiotemporal initialization with VPN dataset (VPN initialization) outperforms the previous initialization method with the inflated 3D ConvNet (I3D) using 2D ImageNet dataset. Our proposed method increased the top-1 video-level accuracy of Kinetics-400 pre-trained model on {Kinetics-400, UCF-101, HMDB-51, ActivityNet} datasets. Especially, the proposed method increased the performance rate of Kinetics-400 pre-trained model by 10.3 pt on ActivityNet. We also report that the relative performance improvements from the baseline are greater in 3D CNNs rather than other models. Our VPN initialization mainly helps to enhance the performance in spatiotemporal 3D kernels. The datasets, codes and pretrained models used in this study will be publicly available(1).