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There is an urgent need for an effective video classification method by meansof a small number of samples. The deficiency of samples could be effectivelyalleviated by generating samples through Generative Adversarial Networks (GAN),but the generation of videos on a typical category remains to be underexploredsince the complex actions and the changeable viewpoints are difficult tosimulate. In this paper, we propose a generative data augmentation method fortemporal stream of the Temporal Segment Networks with the dynamic image. Thedynamic image compresses the motion information of video into a still image,removing the interference factors such as the background. Thus it is easier togenerate images with categorical motion information using GAN. We use thegenerated dynamic images to enhance the features, with regularization achievedas well, thereby to achieve the effect of video augmentation. In order to dealwith the uneven quality of generated images, we propose a Self-Paced Selection(SPS) method, which automatically selects the high-quality generated samples tobe added to the network training. Our method is verified on two benchmarkdatasets, HMDB51 and UCF101. The experimental results show that the method canimprove the accuracy of video classification under the circumstance of sampleinsufficiency and sample imbalance.

Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks