Deep-based Self-refined Face-top Coordination
Honglin Li, Xiaoyang Mao, Mengdi Xu, and Xiaogang Jin
ACM Transactions on Multimedia Computing, Communications, and Applications, 2021, 17(3): Article No. 95.
Intuition-based evaluation on clothes fitting and overview of our proposed work for face-top coordination.
Face-top coordination, which exists in most clothes fitting scenarios, is challenging due to varieties of attributes, implicit correlations, and trade-offs 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 Forest (OPF) method is developed to adapt the results to individual preferences iteratively. Experimental results and user study demonstrate the effectiveness of our method.