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Feature context learning for human parsing

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Abstract

Parsing inconsistency, referring to the scatters and speckles in the parsing results as well as imprecise contours, is a long-standing problem in human parsing. It results from the fact that the pixel-wise classification loss independently considers each pixel. To address the inconsistency issue, we propose in this paper an end-to-end trainable, highly flexible and generic module called feature context module (FCM). FCM explores the correlation of adjacent pixels and aggregates the contextual information embedded in the real topology of the human body. Therefore, the feature representations are enhanced and thus quite robust in distinguishing semantically related parts. Extensive experiments are done with three different backbone models and four benchmark datasets, suggesting that FCM can be an effective and efficient plug-in to consistently improve the performance of existing algorithms without sacrificing the inference speed too much.

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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant No. 2018YFB1004600), National Natural Science Foundation of China (Grant No. 61703171), and Natural Science Foundation of Hubei Province of China (Grant No. 2018CFB199). This work was also supported by Alibaba Group through Alibaba Innovative Research (AIR) Program. The work of Yongchao XU was supported by Young Elite Scientists Sponsorship Program by CAST. The work of Xiang BAI was supported by National Program for Support of Top-Notch Young Professionals and in part by Program for HUST Academic Frontier Youth Team.

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Correspondence to Yongchao Xu.

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Cite this article

Huang, T., Xu, Y., Bai, S. et al. Feature context learning for human parsing. Sci. China Inf. Sci. 62, 220101 (2019). https://doi.org/10.1007/s11432-019-9935-6

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Keywords

  • human parsing
  • context learning
  • fully convolutional networks
  • graph convolutional network
  • semantic segmentation