Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks | |
Zeyu Cui1,2; Zekun Li1,3; Shu Wu1,2; Xiaoyu Zhang1,3; Liang Wang1,2 | |
2019-05 | |
会议日期 | 2019-5-12 |
会议地点 | San Francisco, CA, USA |
关键词 | Graph neural networks Compatibility learning multi-modal |
DOI | 10.1145/3308558.3313444 |
页码 | 307–317 |
英文摘要 | With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question “which item should we select to match with the given fashion items and form a compatible outfit”. The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as a graph. In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories. Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes. To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score with learned node representations. NGNN can not only be used to model outfit compatibility from visual or textual modality but also from multiple modalities. We conduct experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that matches with existing components of outfit; (2) Compatibility prediction: predicting the compatibility scores of given outfits. Experimental results demonstrate the great superiority of our proposed method over others. |
源文献作者 | Association for Computing Machinery |
会议录出版地 | New York, NY, USA |
URL标识 | 查看原文 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44382] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Shu Wu |
作者单位 | 1.University of Chinese Acdemy of Science 2.Chinese Acdemy of Science, Institute of Automation 3.Chinese Acdemy of Science, Institute of Information Engineering |
推荐引用方式 GB/T 7714 | Zeyu Cui,Zekun Li,Shu Wu,et al. Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks[C]. 见:. San Francisco, CA, USA. 2019-5-12. |
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