Data Augmentation in Human-Centric Vision

arXiv:2403.08650v1 Announce Type: new
Abstract: 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 …

abstract analysis arxiv augmentation challenges cs.cv data detection human human-centric kind overfitting parsing pedestrian person research survey tasks type vision