4 papers
Outfit Completion via Conditional Set Transformation
Takuma Nakamura, Yuki Saito, Ryosuke Goto
In this paper, we formulate the outfit completion problem as a set retrieval task and propose a novel framework for solving this problem. The proposal includes a conditional set tr…
Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning
Ryotaro Shimizu, Takuma Nakamura, Masayuki Goto
In this study, we propose a technology called the Fashion Intelligence System based on the visual-semantic embedding (VSE) model to quantify abstract and complex expressions unique…
Exchangeable deep neural networks for set-to-set matching and learning
Yuki Saito, Takuma Nakamura, Hirotaka Hachiya +1
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract feat…
Outfit Generation and Style Extraction via Bidirectional LSTM and Autoencoder
Takuma Nakamura, Ryosuke Goto
When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous…