Fixed-resolution representation network for human pose estimation

Human pose estimation from a single image is a fundamental yet challenging task in computer vision. Most existing methods gradually generated multi-resolution from high-resolution to low-resolution, then recovered the higher resolution from the low resolution and used it to generate final pose heatmaps, such as Hourglass and HRNet and their variants. In this paper, we propose a novel architecture named fixed-resolution representation network for human pose estimation, which maintains fixed-resolution through the whole process to keep rich spatial-structural information. An Improved Pyramid Convolutional Bottleneck (IPCB) is firstly proposed to encode feature maps with multi receptive fields with the same resolution. Secondly, we introduce an efficient channel attention mechanism to enhance the feature extraction and information selection capability of IPCB, making the performance of IPCB better. Thirdly, considering the deviation from using the flip test of reasoning, we use an existing technology: Unbiased Data Processing. Fourthly, due to the change of the model structure and the limited computing resources, we introduce an iterative retraining strategy to solve the problem of pre-training. We empirically demonstrate the effectiveness of our method and achieve a competitive performance with 1.7M parameters and 3G FLOPs, 89.5 (PCKh@0.5) and 92.7 (PCK@0.2) respectively, compared with the state-of-the-art methods on the benchmark dataset: the MPII and LSP key points detection dataset.