Data Augmentation in Human-Centric Vision
Wentao Jiang Yige Zhang Shaozhong Zheng Si Liu Shuicheng Yan
This survey presents a comprehensive analysis of data augmentation techniques
in human-centric vision tasks, a first of its kind in the field. It delves into
a wide range of research areas including person ReID, human parsing, human pose
estimation, and pedestrian detection, addressing the significant challenges
posed by overfitting and limited training data in these domains. Our work
categorizes data augmentation methods into two main types: data generation and
data perturbation. Data generation covers techniques like graphic engine-based
generation, generative model-based generation, and data recombination, while
data perturbation is divided into image-level and human-level perturbations.
Each method is tailored to the unique requirements of human-centric tasks, with
some applicable across multiple areas. Our contributions include an extensive
literature review, providing deep insights into the influence of these
augmentation techniques in human-centric vision and highlighting the nuances of
each method. We also discuss open issues and future directions, such as the
integration of advanced generative models like Latent Diffusion Models, for
creating more realistic and diverse training data. This survey not only
encapsulates the current state of data augmentation in human-centric vision but
also charts a course for future research, aiming to develop more robust,
accurate, and efficient human-centric vision systems.