To address the problem of training on small datasets for action recognition
tasks, most prior works are either based on a large number of training samples
or require pre-trained models transferred from other large datasets to tackle
overfitting problems. However, it limits the research within organizations that
have strong computational abilities. In this work, we try to propose a
data-efficient framework that can train the model from scratch on small
datasets while achieving promising results. Specifically, by introducing a 3D
central difference convolution operation, we proposed a novel C3D neural
network-based two-stream (Rank Pooling RGB and Optical Flow) framework for the
task. The method is validated on the action recognition track of the ECCV 2020
VIPriors challenges and got the 2nd place (88.31%). It is proved that our
method can achieve a promising result even without a pre-trained model on large
scale datasets. The code will be released soon.

本文提出了一个基于 C3D 神经网络的双流框架，引入了 3D 中心差分卷积操作，能够在小数据集上从零开始训练模型并取得良好的结果，同时在 ECCV 2020 VIPriors 竞赛中获得第二名（88.31%）。