  Human parsing has been extensively studied recently due to its wide
applications in many important scenarios. Mainstream fashion parsing models
focus on parsing the high-resolution and clean images. However, directly
applying the parsers trained on benchmarks to a particular application scenario
in the wild, e.g., a canteen, airport or workplace, often gives
non-satisfactory performance due to domain shift. In this paper, we explore a
new and challenging cross-domain human parsing problem: taking the benchmark
dataset with extensive pixel-wise labeling as the source domain, how to obtain
a satisfactory parser on a new target domain without requiring any additional
manual labeling? To this end, we propose a novel and efficient cross-domain
human parsing model to bridge the cross-domain differences in terms of visual
appearance and environment conditions and fully exploit commonalities across
domains. Our proposed model explicitly learns a feature compensation network,
which is specialized for mitigating the cross-domain differences. A
discriminative feature adversarial network is introduced to supervise the
feature compensation to effectively reduce the discrepancy between feature
distributions of two domains. Besides, our model also introduces a structured
label adversarial network to guide the parsing results of the target domain to
follow the high-order relationships of the structured labels shared across
domains. The proposed framework is end-to-end trainable, practical and scalable
in real applications. Extensive experiments are conducted where LIP dataset is
the source domain and 4 different datasets including surveillance videos,
movies and runway shows are evaluated as target domains. The results
consistently confirm data efficiency and performance advantages of the proposed
method for the cross-domain human parsing problem.
