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Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
Zeyu Cui1,2; Zekun Li2,3; Shu Wu1,2; Xiaoyu Zhang2,3; Liang Wang1,2
Conference NameThe World Wide Web Conference (WWW)
Conference Date2019-5-12
Conference PlaceSan Francisco, CA, USA
Author of SourceAssociation for Computing Machinery
Publication PlaceNew York, NY, USA

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.

KeywordGraph neural networks Compatibility learning multi-modal
MOST Discipline Catalogue工学 ; 工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Funding ProjectNational Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61871378] ; National Natural Science Foundation of China[61772528]
Sub direction classification图像视频处理与分析
Citation statistics
Cited Times:37[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Corresponding AuthorShu Wu
Affiliation1.Chinese Acdemy of Science, Institute of Automation
2.University of Chinese Acdemy of Science
3.Chinese Acdemy of Science, Institute of Information Engineering
Recommended Citation
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]//Association for Computing Machinery. New York, NY, USA,2019:307–317.
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