Exploring Temporally Dynamic Data Augmentation for Video Recognition

TL;DR

This paper introduces DynaAugment, a novel data augmentation framework for video recognition that dynamically varies augmentation magnitudes over time using Fourier Sampling, leading to improved performance across diverse video tasks.

Contribution

The paper proposes DynaAugment, a simple yet effective method that models temporal variations in video augmentation, extending existing static methods with a Fourier Sampling mechanism and an expanded search space.

Findings

DynaAugment improves accuracy on multiple video datasets and tasks.

Temporal variation in augmentation enhances model robustness.

The method outperforms static augmentation approaches.

Abstract

Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been rarely explored despite its effectiveness. Few existing augmentation recipes for video recognition naively extend the image augmentation methods by applying the same operations to the whole video frames. Our main idea is that the magnitude of augmentation operations for each frame needs to be changed over time to capture the real-world video's temporal variations. These variations should be generated as diverse as possible using fewer additional hyper-parameters during training. Through this motivation, we propose a simple yet effective video data augmentation framework, DynaAugment. The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations. DynaAugment also includes an extended search space suitable for video for automatic data augmentation methods. DynaAugment experimentally demonstrates that there are additional performance rooms to be improved from static augmentations on diverse video models. Specifically, we show the effectiveness of DynaAugment on various video datasets and tasks: large-scale video recognition (Kinetics-400 and Something-Something-v2), small-scale video recognition (UCF- 101 and HMDB-51), fine-grained video recognition (Diving-48 and FineGym), video action segmentation on Breakfast, video action localization on THUMOS'14, and video object detection on MOT17Det. DynaAugment also enables video models to learn more generalized representation to improve the model robustness on the corrupted videos.