Instance-Level Data Augmentation for Multi-Person Pose Estimation: Improving Recognition of Individuals at Different Scales

In the realm of multi-person pose estimation, bottom-up approaches often tackle the task of identifying human keypoints for individuals at various scales within a given image. However, in practical scenarios, algorithms tend to perform better in recognizing larger individuals. This is primarily attributed to the increased pixel count and richer feature information available. Conversely, recognizing smaller-scale individuals poses a notably more challenging task.To address this challenge, we propose an instance-level data augmentation strategy. This strategy involves applying transformations to individual instances rather than the entire image. Its primary objectives are to enhance dataset diversity, refine the distribution of different human scale samples in the training data, and augment the representation of medium-sized human instances in the training set. The goal of this augmentation strategy is to empower the model to better recognize finer details.Our extensive experiments, conducted on the HigherHRNet benchmark model, demonstrate the effectiveness of our approach in improving accuracy, particularly in the recognition of mediumsized individuals. Importantly, these improvements are achieved without introducing additional model complexity or requiring additional image collection.