Journal Article

Smoothness-based consistency learning for macaque pose estimation

Deng, ShiXiong, Xue, Ping

2023

Overview

Macaques are a rare substitute and play an important role in study of human psychology and spiritual science. Accurate estimation of macaque pose information is key to these studies, macaque pose estimation remains to be hindered by the scarcity of labeled images. To address this problem, this work introduces a novel semi-supervised approach called smoothness-based spatio-temporal consistency learning (SSTCL) and a dual network structure (DNS) to leverage the amounts of unlabeled real images. Specifically, the SSTCL introduces the smoothness assumption to help the model generalize from the labeled training images to the unlabeled images, and the spatio-temporal consistency is designed to leverage both spatial and temporal consistencies to pick the most reliable pseudo-labels. Moreover, a dual network structure (DNS) is proposed to empower the model the ability of self-correction, which can prevent the degeneration caused by the noisy pseudo-labels in semi-supervised learning. In ablation experiments, the effectiveness of DNS for pseudo-label quality assurance is demonstrated. We evaluate the proposed method on the public OpenMonkeyPose dataset, the results show that the proposed method can achieve competitive performance while using less labeled images, and the final accuracy surpasses the strong baseline HRNet-w48 of 2.1 AP.

• Publisher: Springer London,Springer Nature B.V
• Subject: Ablation / Accuracy / Computer Imaging / Computer Science / Datasets / Deep learning / Degeneration / Domain names / Image Processing and Computer Vision / Labels / Multimedia Information Systems / Original Paper / Pattern Recognition and Graphics / Pose estimation / Quality assurance / Semi-supervised learning / Sensors / Signal,Image and Speech Processing / Smoothness / Vision