  Occlusion presents a significant challenge in human pose estimation. The
challenges posed by occlusion can be attributed to the following factors: 1)
Data: The collection and annotation of occluded human pose samples are
relatively challenging. 2) Feature: Occlusion can cause feature confusion due
to the high similarity between the target person and interfering individuals.
3) Inference: Robust inference becomes challenging due to the loss of complete
body structural information. The existing methods designed for occluded human
pose estimation usually focus on addressing only one of these factors. In this
paper, we propose a comprehensive framework DAG (Data, Attention, Graph) to
address the performance degradation caused by occlusion. Specifically, we
introduce the mask joints with instance paste data augmentation technique to
simulate occlusion scenarios. Additionally, an Adaptive Discriminative
Attention Module (ADAM) is proposed to effectively enhance the features of
target individuals. Furthermore, we present the Feature-Guided Multi-Hop GCN
(FGMP-GCN) to fully explore the prior knowledge of body structure and improve
pose estimation results. Through extensive experiments conducted on three
benchmark datasets for occluded human pose estimation, we demonstrate that the
proposed method outperforms existing methods. Code and data will be publicly
available.
