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上传时间: 2020-05-27
详细说明：Human parsing has been extensively studied recently (Yamaguchi et al. 2012; Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution
and clean images. However, directly applying the parsers
trained on benchmarks of high-quality samples 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 crossdomain 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 reduces the discrepancy between feature distributions
of two domains. Besides, our proposed 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 without any annotations, are evaluated as target
domains. The results consistently confirm data efficiency and
performance advantages of the proposed method for the challenging cross-domain human parsing problem.
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