  Multi-human parsing is an image segmentation task necessitating both
instance-level and fine-grained category-level information. However, prior
research has typically processed these two types of information through
separate branches and distinct output formats, leading to inefficient and
redundant frameworks. This paper introduces UniParser, which integrates
instance-level and category-level representations in three key aspects: 1) we
propose a unified correlation representation learning approach, allowing our
network to learn instance and category features within the cosine space; 2) we
unify the form of outputs of each modules as pixel-level segmentation results
while supervising instance and category features using a homogeneous label
accompanied by an auxiliary loss; and 3) we design a joint optimization
procedure to fuse instance and category representations. By virtual of unifying
instance-level and category-level output, UniParser circumvents manually
designed post-processing techniques and surpasses state-of-the-art methods,
achieving 49.3% AP on MHPv2.0 and 60.4% AP on CIHP. We will release our source
code, pretrained models, and online demos to facilitate future studies.
