Color Fashion

by Si LIU,Jiashi FENG,Csaba DOMOKOS,Hui XU,Junshi HUANG,Zhenzhen HU,Shuicheng YANUnknown

Color Fashion

In this paper we address the problem of automatically parsing the fashion images with weak supervision from the user-generated color-category tags such as “red jeans” and “white T-shirt”. This problem is very challenging due to the large diversity of fashion items and the absence of pixel-level tags, which make the traditional fully supervised algorithms inapplicable. To solve the problem, we propose to combine the human pose estimation module, the MRF-based color and category inference module and the (super)pixel-level category classifier learning module to generate multiple well-performing category classifiers, which can be directly applied to parse the fashion items in the images. Besides, all the training images are parsed with color-category labels and the human poses of the images are estimated during the model learning phase in this work. We also construct a new fashion dataset called Colorful-Fashion, in which all 2; 682 images are labeled with pixel-level color-category labels. Extensive experiments on this dataset clearly show the effectiveness of the proposed method for the weakly supervised fashion parsing task.

Dataset Attributes

Label SVG
TasksClassification
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CategoriesHumans, Clothing, Fashion
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SensorRGB Camera