Cluster-wise learning network for multi-person pose estimation

Highlights：

• Feature aggregation. To enhance feature passing from shallow stack to deep stack, we aggregate information from different branches. The in-branch aggregation enriches the detection features in each branch by absorbing the holistic human region attention. The cross-branch aggregation further strengthens the detection features by fusing global and local context information between dense and sparse branches.

• Cluster-wise tag embedding. To better grouping the detected keypoints into instances, our network embeds relationships among the intra-cluster and inter-cluster keypoints with offset learning, which not only benefits the instance grouping but also individual keypoint identification.

摘要

•Cluster-wise keypoint detection. Instead of detect each keypoint separately, our network predicts multi-peak heatmaps for clusters of dense and sparse keypoints, which exploits global and local contextual information to improve the detection robustness.•Feature aggregation. To enhance feature passing from shallow stack to deep stack, we aggregate information from different branches. The in-branch aggregation enriches the detection features in each branch by absorbing the holistic human region attention. The cross-branch aggregation further strengthens the detection features by fusing global and local context information between dense and sparse branches.•Cluster-wise tag embedding. To better grouping the detected keypoints into instances, our network embeds relationships among the intra-cluster and inter-cluster keypoints with offset learning, which not only benefits the instance grouping but also individual keypoint identification.