Aggregated pyramid gating network for human pose estimation without pre-training

•We present a multi-stage residual feature pyramid gating strategy which designs soft gates to select and reinforce multi-scale semantic features.•We introduce an image pyramid gating strategy to preserve natural information to enhance network discrimination in an attention-based fashion.•Our method attains the best performance without pre-training and can be readily combined with general frameworks without structure modifications. Specifically, the prediction accuracy consistently achieves over 3% improvements than the baseline (without pre-training) under various model configurations.