关键词: 深度学习, 卷积网络, 高分辨率特征表示, 人体关键点检测, 注意力机制

Abstract:

Human keypoint detection as a pixel-level detection task, high resolution feature map representations are usually adopted to enhance the detection effect for keypoint regression in deep learning. To solve the problem of large number of parameters and high computation requirement caused by maintaining high-resolution representations throughout the process in High-Resolution Network（HRNet）, Gattneck module and Gattblock module are proposed, which are two lightweight basic network modules based on HRNet and construct a lightweight human keypoint detection network, namely Ghost-attention Network（GattNet）. GattNet uses linear transformation to generate redundant feature maps and adopts channelwise attention mechanism to make attention weights reweight activations in different channels, which reduces the number of HRNet parameters by 41.5% and the computational complexity by 36.7%. The validation results on MS COCO 2017 dataset show that GattNet can effectively reduce the number of parameters and the computational cost while preserving the accuracy.

Key words: deep learning, convolutional network, high-resolution representation, human keypoint detection, attention mechanism