Multi-human Parsing Based on Dynamic Convolution

Multi-human parsing is attracting more and more attention due to its wide application, which not only needs to differentiate different human instances but also categorizes each pixel within the human. In this work, we improve the performance of the mask prediction branch of Nondiscriminatory Treatment between Humans and Parts for Human Parsing (NTHP), which regards both the humans and parts as objects and directly executes instance segmentation on both of them using the same structure. Specifically, we learn the mask feature and the mask kernel separately, and the mask feature is convolved by the mask kernel to obtain the binary mask prediction. Besides, to obtain translation-variance and better performance, we design a Position-sensitive Global Context (PGC) block and insert it into the mask feature module. Experiments show that our network performs superiorly against state-of-the-art methods on the MHP v2.0 and PASCAL-Person-Part datasets.