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

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAAAI press
Pages2207-2215
Number of pages9
ISBN (Electronic)9781713835974
ISBN (Print)9781577358664
DOIs
Publication statusPublished - 18 May 2021

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number3
Volume35
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468
NameAAAI-21/ IAAI-21/ EAAI-21 Proceedings

User-Defined Keywords

  • Segmentation

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