Paper Info
Title
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
Abstract
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the assumption of Gaussian feature distribution, conventional feature distribution matching methods usually match the mean and standard deviation of features. However, the feature distributions of real-world data are usually much more complicated than Gaussian, which cannot be accurately matched by using only the first-order and second-order statistics, while it is computationally prohibitive to use high-order statistics for distribution matching. In this work, we, for the first time to our best knowledge, propose to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching (EHM) in the image feature space. Particularly, a fast EHM algorithm, named Sort-Matching, is employed to perform EFDM in a plug-and-play manner with minimal cost. The effectiveness of our proposed EFDM method is verified on a variety of AST and DG tasks, demonstrating new state-of-the-art results. Codes are available at https://github.com/YBZh/EFDM.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.00787
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Transfer/low-shot/long-tail learning, Image and video synthesis and generation, Recognition: detection,categorization,retrieval, Statistical methods
Conference
2022
Issue
ISSN
Citations 
1
CVPR2022 camera ready
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yabin Zhang121.04
Minghan Li200.34
Ruihuang Li301.35
Kui Jia4143964.32
Lei Zhang516326543.99