# Keypoint Based Weakly Supervised Human Parsing

arXiv: https://arxiv.org/abs/1809.05285 · HTML: https://mlsanity.com/paper/1809.05285/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2203.01324/) (2022)
- [Learning Meta-class Memory for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2108.02958/) (2021)
- [MV-TON: Memory-based Video Virtual Try-on network](https://mlsanity.com/paper/2108.07502/) (2021)
- [Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2207.09084/) (2022)
- [Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation](https://mlsanity.com/paper/2108.07413/) (2021)
- [Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2303.05164/) (2023)
- [Long-tailed Recognition by Learning from Latent Categories](https://mlsanity.com/paper/2206.01010/) (2022)
- [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022)

## Abstract (arXiv)

Fully convolutional networks (FCN) have achieved great success in human parsing in recent years. In conventional human parsing tasks, pixel-level labeling is required for guiding the training, which usually involves enormous human labeling efforts. To ease the labeling efforts, we propose a novel weakly supervised human parsing method which only requires simple object keypoint annotations for learning. We develop an iterative learning method to generate pseudo part segmentation masks from keypoint labels. With these pseudo masks, we train an FCN network to output pixel-level human parsing predictions. Furthermore, we develop a correlation network to perform joint prediction of part and object segmentation masks and improve the segmentation performance. The experiment results show that our weakly supervised method is able to achieve very competitive human parsing results. Despite our method only uses simple keypoint annotations for learning, we are able to achieve comparable performance with fully supervised methods which use the expensive pixel-level annotations.