Hierarchical Information Passing Based Noise-Tolerant Hybrid Learning for Semi-Supervised Human Parsing

Authors

  • Yunan Liu School of Computer Science and Engineering, Nanjing University of Science and Technology
  • Shanshan Zhang School of Computer Science and Engineering, Nanjing University of Science and Technology
  • Jian Yang School of Computer Science and Engineering, Nanjing University of Science and Technology
  • PongChi Yuen Department of Computer Science, Hong Kong Baptist University

Keywords:

Segmentation

Abstract

Deep learning based human parsing methods usually require a large amount of training data to reach high performance. However, it is costly and time-consuming to obtain manually annotated high quality labels for a large scale dataset. To alleviate annotation efforts, we propose a new semi-supervised human parsing method for which we only need a small number of labels for training. First, we generate high quality pseudo labels on unlabeled images using a hierarchical information passing network (HIPN), which reasons human part segmentation in a coarse to fine manner. Furthermore, we develop a noise-tolerant hybrid learning method, which takes advantage of positive and negative learning to better handle noisy pseudo labels. When evaluated on standard human parsing benchmarks, our HIPN achieves a new state-of-the-art performance. Moreover, our noise-tolerant hybrid learning method further improves the performance and outperforms the state-of-the-art semi-supervised method (i.e. GRN) by 4.47 points w.r.t mIoU on the LIP dataset.

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Published

2021-05-18

How to Cite

Liu, Y., Zhang, S., Yang, J., & Yuen, P. (2021). Hierarchical Information Passing Based Noise-Tolerant Hybrid Learning for Semi-Supervised Human Parsing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 2207-2215. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16319

Issue

Section

AAAI Technical Track on Computer Vision II