Multi-task Curriculum Transfer Deep Learning of Clothing Attributes
Multi-task Curriculum Transfer Deep Learning of Clothing Attributes
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DOI：
10.1109/wacv.2017.64
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Qi Dong;S. Gong;Xiatian Zhu
中科院分区：
文献类型：
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作者：
Qi Dong;S. Gong;Xiatian Zhu
Recognising detailed clothing characteristics (finegrained attributes) in unconstrained images of people inthe-wild is a challenging task for computer vision, especially when there is only limited training data from the wild whilst most data available for model learning are captured in well-controlled environments using fashion models (well lit, no background clutter, frontal view, high-resolution). In this work, we develop a deep learning framework capable of model transfer learning from well-controlled shop clothing images collected from web retailers to in-the-wild images from the street. Specifically, we formulate a novel Multi-Task Curriculum Transfer (MTCT) deep learning method to explore multiple sources of different types of web annotations with multi-labelled fine-grained attributes. Our multi-task loss function is designed to extract more discriminative representations in training by jointly learning all attributes, and our curriculum strategy exploits the staged easy-to-hard transfer learning motivated by cognitive studies. We demonstrate the advantages of the MTCT model over the state-of-the-art methods on the X-Domain benchmark, a large scale clothing attribute dataset. Moreover, we show that the MTCT model has a notable advantage over contemporary models when the training data size is small.
