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

Yunan Liu, Shanshan Zhang, Jian Yang, Pong Chi Yuen

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAAAI press
Number of pages9
ISBN (Electronic)9781713835974
ISBN (Print)9781577358664
Publication statusPublished - 18 May 2021

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468
NameAAAI-21/ IAAI-21/ EAAI-21 Proceedings

User-Defined Keywords

  • Segmentation


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