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

• Main Authors: Liu, Yunan, Zhang, Shanshan, Yang, Jian, Yuen, PongChi
• Format: Conference Proceeding
• Language: English
• ISSN: 2159-5399; 2374-3468
• DOI: 10.1609/aaai.v35i3.16319

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.