AFD-Net: Aggregated Feature Difference Learning for Cross-Spectral Image Patch Matching

Dou Quan, Xuefeng Liang, Shuang Wang, Shaowei Wei, Yanfeng Li, Ning Huyan, Licheng Jiao; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 3017-3026

Abstract


Image patch matching across different spectral domains is more challenging than in a single spectral domain. We consider the reason is twofold: 1. the weaker discriminative feature learned by conventional methods; 2. the significant appearance difference between two images domains. To tackle these problems, we propose an aggregated feature difference learning network (AFD-Net). Unlike other methods that merely rely on the high-level features, we find the feature differences in other levels also provide useful learning information. Thus, the multi-level feature differences are aggregated to enhance the discrimination. To make features invariant across different domains, we introduce a domain invariant feature extraction network based on instance normalization (IN). In order to optimize the AFD-Net, we borrow the large margin cosine loss which can minimize intra-class distance and maximize inter-class distance between matching and non-matching samples. Extensive experiments show that AFD-Net largely outperforms the state-of-the-arts on the cross-spectral dataset, meanwhile, demonstrates a considerable generalizability on a single spectral dataset.

Related Material


[pdf]
[bibtex]
@InProceedings{Quan_2019_ICCV,
author = {Quan, Dou and Liang, Xuefeng and Wang, Shuang and Wei, Shaowei and Li, Yanfeng and Huyan, Ning and Jiao, Licheng},
title = {AFD-Net: Aggregated Feature Difference Learning for Cross-Spectral Image Patch Matching},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}