XPose: eXplainable Human Pose Estimation

arXiv:2403.12370v1 Announce Type: new
Abstract: Current approaches in pose estimation primarily concentrate on enhancing model architectures, often overlooking the importance of comprehensively understanding the rationale behind model decisions. In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation. This integration aims to elucidate the individual contribution of each keypoint to final prediction, thereby elevating the model's transparency and interpretability. Conventional XAI techniques have predominantly addressed tasks with single-target tasks like classification. Additionally, …

abstract architectures arxiv cs.cv current decisions explainable ai framework human importance integration novel paper type understanding xai