Deep-based Self-refined Face-top Coordination
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Published:2021-07-22
Issue:3
Volume:17
Page:1-23
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ISSN:1551-6857
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Container-title:ACM Transactions on Multimedia Computing, Communications, and Applications
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language:en
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Short-container-title:ACM Trans. Multimedia Comput. Commun. Appl.
Author:
Li Honglin1,
Mao Xiaoyang2,
Xu Mengdi3,
Jin Xiaogang3
Affiliation:
1. Quanzhou Medical College, China
2. University of Yamanashi, Japan
3. State Key Lab of CAD&CG, Zhejiang University, China
Abstract
Face-top coordination, which exists in most clothes-fitting scenarios, is challenging due to varieties of attributes, implicit correlations, and tradeoffs between general preferences and individual preferences. We present a Deep-Based Self-Refined (DBSR) system to simulate face-top coordination based on intuition evaluation. To this end, we first establish a well-coordinated face-top (WCFT) dataset from fashion databases and communities. Then, we use a jointly trained CNN Deep Canonical Correlation Analysis (DCCA) method to bridge the semantic face-top gap based on the WCFT dataset to deal with general preferences. Subsequently, an irrelevance-based Optimum-path Forest (OPF) method is developed to adapt the results to individual preferences iteratively. Experimental results and user study demonstrate the effectiveness of our method.
Funder
National Key R&D Program of China
Ningbo Major Special Projects of the “Science and Technology Innovation 2025”
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture