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C-DLinkNet: considering multi-level semantic features for human parsing

2020-04-05 11:12:12
Yu Lu, Muyan Feng, Ming Wu, Chuang Zhang

Abstract

Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human. The challenge of human parsing is to extract effective semantic features to resolve deformation and multi-scale variations. In this work, we proposed an end-to-end model called C-DLinkNet based on LinkNet, which contains a new module named Smooth Module to combine the multi-level features in Decoder part. C-DLinkNet is capable of producing competitive parsing performance compared with the state-of-the-art methods with smaller input sizes and no additional information, i.e., achiving mIoU=53.05 on the validation set of LIP dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2001.11690

PDF

https://arxiv.org/pdf/2001.11690


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