LAUSR

We tackle the issue of data imbalance between different poses in the human pose estimation problem. We explore unusual poses that are rare which occupy a small portion in a pose dataset. In order to identify a rare pose without additional learning, a simple $K$ -means clustering algorithm is applied to a given dataset. Experimental results on MPII and COCO datasets show that outliers which are far from the nearest cluster center can be defined as rare poses and the accuracy decreases as the distance between the data point and the cluster center increases. In order to improve the performance on the rare poses, we proposed three methods for the problem of data scarcity, which are addition of rare pose duplicates, addition of synthetic rare pose data and weighted loss based on the distance from the cluster. In the proposed methods, the highest increasing score is 13.5 mAP at the rare pose data.