  Multi-human parsing aims to segment every body part of every human instance.
Nearly all state-of-the-art methods follow the "detection first" or
"segmentation first" pipelines. Different from them, we present an end-to-end
and box-free pipeline from a new and more human-intuitive perspective. In
training time, we directly do instance segmentation on humans and parts. More
specifically, we introduce a notion of "indiscriminate objects with categorie"
which treats humans and parts without distinction and regards them both as
instances with categories. In the mask prediction, each binary mask is obtained
by a combination of prototypes shared among all human and part categories. In
inference time, we design a brand-new grouping post-processing method that
relates each part instance with one single human instance and groups them
together to obtain the final human-level parsing result. We name our method as
Nondiscriminatory Treatment between Humans and Parts for Human Parsing (NTHP).
Experiments show that our network performs superiorly against state-of-the-art
methods by a large margin on the MHP v2.0 and PASCAL-Person-Part datasets.
