Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks

TL;DR

This paper introduces a self-supervised augmentation technique for GANs that enhances video representations, significantly improving human activity recognition accuracy on multiple datasets.

Contribution

It presents a novel self-supervised augmentation method for GANs that improves video encoding for activity recognition tasks.

Findings

Achieved over 4% increase in top-1 accuracy on UCF101 dataset.

Demonstrated superiority over baseline methods in human activity recognition.

Ablation study confirmed the effectiveness of different transformations.

Abstract

This article proposes a novel approach for augmenting generative adversarial network (GAN) with a self-supervised task in order to improve its ability for encoding video representations that are useful in downstream tasks such as human activity recognition. In the proposed method, input video frames are randomly transformed by different spatial transformations, such as rotation, translation and shearing or temporal transformations such as shuffling temporal order of frames. Then discriminator is encouraged to predict the applied transformation by introducing an auxiliary loss. Subsequently, results prove superiority of the proposed method over baseline methods for providing a useful representation of videos used in human activity recognition performed on datasets such as KTH, UCF101 and Ball-Drop. Ball-Drop dataset is a specifically designed dataset for measuring executive functions in children through physically and cognitively demanding tasks. Using features from proposed method instead of baseline methods caused the top-1 classification accuracy to increase by more then 4%. Moreover, ablation study was performed to investigate the contribution of different transformations on downstream task.