Name
Title | ||
|---|---|---|
Afd-Net: Aggregated Feature Difference Learning For Cross-Spectral Image Patch Matching |
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, mean-while, demonstrates a considerable generalizability on a single spectral dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCV.2019.00311 | 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) |
Field | DocType | Volume |
Computer vision,Pattern recognition,Computer science,Artificial intelligence | Conference | 2019 |
Issue | ISSN | Citations |
1 | 1550-5499 | 1 |
PageRank | References | Authors |
0.35 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dou Quan | 1 | 2 | 3.06 |
| Xuefeng Liang | 2 | 2 | 1.71 |
| Shuang Wang | 3 | 8 | 2.02 |
| Shaowei Wei | 4 | 2 | 1.03 |
| Yanfeng Li | 5 | 2 | 0.69 |
| Ning Huyan | 6 | 10 | 2.79 |
| Licheng Jiao | 7 | 5698 | 475.84 |