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Shuhui Jiang

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2 papers

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IJCAI Conference 2017 Conference Paper

Fashion Style Generator

Shuhui Jiang
Yun Fu

In this paper, we focus on a new problem: applying artificial intelligence to automatically generate fashion style images. Given a basic clothing image and a fashion style image (e. g. , leopard print), we generate a clothing image with the certain style in real time with a neural fashion style generator. Fashion style generation is related to recent artistic style transfer works, but has its own challenges. The synthetic image should preserve the similar design as the basic clothing, and meanwhile blend the new style pattern on the clothing. Neither existing global nor patch based neural style transfer methods could well solve these challenges. In this paper, we propose an end-to-end feed-forward neural network which consists of a fashion style generator and a discriminator. The global and patch based style and content losses calculated by the discriminator alternatively back-propagate the generator network and optimize it. The global optimization stage preserves the clothing form and design and the local optimization stage preserves the detailed style pattern. Extensive experiments show that our method outperforms the state-of-the-arts.

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AAAI Conference 2016 Conference Paper

Consensus Style Centralizing Auto-Encoder for Weak Style Classification

Shuhui Jiang
Ming Shao
Chengcheng Jia
Yun Fu

Style classiﬁcation (e. g. , architectural, music, fashion) attracts an increasing attention in both research and industrial ﬁelds. Most existing works focused on low-level visual features composition for style representation. However, little effort has been devoted to automatic mid-level or high-level style features learning by reorganizing low-level descriptors. Moreover, styles are usually spread out and not easy to differentiate from one to another. In this paper, we call these less representative images as weak style images. To address these issues, we propose a consensus style centralizing autoencoder (CSCAE) to extract robust style features to facilitate weak style classiﬁcation. CSCAE is the ensemble of several style centralizing auto-encoders (SCAEs) with consensus constraint. Each SCAE centralizes each feature of certain category in a progressive way. We apply our method in fashion style classiﬁcation and manga style classiﬁcation as two example applications. In addition, we collect a new dataset, Online Shopping, for fashion style classiﬁcation evaluation, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.

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