Cross-domain Human Parsing via Adversarial Feature and Label Adaptation

Understand

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.