Conditional Feature Coupling Network for Multi-persons Clothing Parsing.
ADVANCES IN MULTIMEDIA INFORMATION PROCESSING, PT I(2018)
摘要
Clothing parsing provides some significant cues to analyze the dressing collocation and occasion. In this paper, we propose a novel clothing parsing framework with deep end-to-end conditional feature coupling network for the photographic multi-persons in the fashion scene, and annotate a multi-persons clothing dataset for the effectiveness demonstration. Our parsing framework has three sub-networks, including the coarse parsing network (CPN), the multi-pose feature network (MFN) and the coupling residual network (CRN). CPN and MFN generate a coarse segmentation intermediary and 28 pose-indicated heat maps, respectively. CRN receives these auxiliary information and generates the fine-tuning clothing parsing result. To verify the generality and effectiveness of our parsing framework, we compare our method with the state-of-the-art parsing and segmentation methods such as Deeplab [2] and Co-CNN [7] on our multi-persons clothing dataset and some fashion clothing benchmarks. Experimental evaluations on these datasets demonstrate that our framework has a superior performance in the parsing task. In particular, our CFCN achieves 88.74% accuracy on the multi-persons clothing dataset, which is significantly higher than 86.50% by Deeplab. The project is available at https://github. com/suzhuoi/CFCNet.
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关键词
Clothing parsing,Deep learning,Coupling network
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