Humble Teacher and Eager Student: Dual Network Learning for Semi-supervised 2D Human Pose Estimation

Image credit: Unsplash

Abstract

Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, the methods degenerate and predict every pixel in unlabeled images as background.

Publication
Arxiv
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