An efficient framework for outfit compatibility prediction towards occasion

Autor: Anh H. Vo, Tung B. T. Le, Huy V. Pham, Bao T. Nguyen
Rok vydání: 2023
Předmět:
Zdroj: Neural Computing and Applications. 35:14213-14226
ISSN: 1433-3058
0941-0643
DOI: 10.1007/s00521-023-08431-1
Popis: Nowadays, in a communicating society, fashion is an integral part of a human life, and it is more comfortable and confident when people dress well. Outfit compatibility is not only a combination of different items but also regarding various aspects, such as style, user preferences, and specific occasions. Most of the existing works lead to address the outfit compatibility concerning only style or user preferences, and have no regard for occasions. In this paper, we propose an efficient method for both outfit compatibility and the fill-in-the-blank tasks according to specific occasions. To this end, we utilized an auxiliary classification branch to learn the significantly important features regarding specific occasions. Besides, a sequence to sequence approach is also applied to learn the relationship of different items along with a visual semantic space, which is able to learn the connection between visual features and their semantic presentation. To demonstrate the effectiveness of the proposed method, we conduct experiments on our newly collected Shoplook-Occasion dataset. The experimental results indicate that our proposed method improved the AUC metric from 0.02 to 0.15% and from 0.5 to 4% on accuracy, compared with other approaches for outfit compatibility problem conditioning on specific occasions.
Databáze: OpenAIRE
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