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Improving Human Parsing by Extracting Global Information Using the Non-Local Operation

Human parsing has recently attracted considerable interests due to its wide application potentials. However, developing an accurate human parsing system is still a challenge for researchers. In this paper, we demonstrate that global information are critical for accurate prediction by applying a non-local operation for effectively extracting global information. Meanwhile several training data refinement methodologies are proposed to further boost the performance. Benefiting from all the approaches, the proposed single human parsing model NLGINet achieves the state-of-the-art segmentation accuracy on two human parsing benchmark datasets LIP and Pascal-Person-Parts.