Update README.md

Author: layumi

State-of-the-art/README.md

@@ -48,6 +48,7 @@ Drone <-> Satellite
 |MCCG| 89.64 | 91.32 | 94.30 | 89.39 | Tianrui Shen, Yingmei Wei, Lai Kang, Shanshan Wan and Yee-Hong Yang. MCCG: A ConvNeXt-based Multiple-Classifier Method for Cross-view Geo-localization. TCSVT 2023 [[Code]](https://github.com/mode-str/crossview) |
 | MFJR | 91.87 | 93.15 | 95.29 | 91.51 | Ge, F., Zhang, Y., Wang, L., Liu, W., Liu, Y., Coleman, S., & Kerr, D. (2024). Multi-level Feedback Joint Representation Learning Network Based on Adaptive Area Elimination for Cross-view Geo-localization. IEEE Transactions on Geoscience and Remote Sensing. |
 |Sample4Geo| 92.65 | 93.81 | 95.14 | 91.39 | Fabian Deuser, Konrad Habel, Norbert Oswald. Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation. ICCV 2023 [[Paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Deuser_Sample4Geo_Hard_Negative_Sampling_For_Cross-View_Geo-Localisation_ICCV_2023_paper.html) [[Code]](https://github.com/Skyy93/Sample4Geo) |
+| MFRGN | 94.33 | 95.24	| 96.15 | 93.94 | Wang, Y., Zhang, J., Wei, R., Gao, W., & Wang, Y. MFRGN: Multi-scale Feature Representation Generalization Network for Ground-to-Aerial Geo-localization. ACM MM2024. [[Code]](https://github.com/ytao-wang/MFRGN) 
 
 Ground <-> Satellite
 
@@ -135,6 +136,7 @@ Ground <-> Satellite
 | FI* | 86.79 | - | - | - | Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Anderi Georgecu, An Tran, Hannes Kruppa, See-Kiong Ng, Roger Zimmermann. Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery. ACM MM 2022 [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3503161.3548102) |
 |SAIG-D*| 89.06 | 96.11 | 97.08 | 98.89 | Yingying Zhu, Hongji Yang, Yuxin Lu and Qiang Huang. Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization. ArXiv 2023|
 |Sample4Geo| 90.81 | 96.74 | 97.48 | 98.77 | Fabian Deuser, Konrad Habel, Norbert Oswald. Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation. ICCV 2023 [[Paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Deuser_Sample4Geo_Hard_Negative_Sampling_For_Cross-View_Geo-Localisation_ICCV_2023_paper.html) [[Code]](https://github.com/Skyy93/Sample4Geo) |
+| MFRGN | 91.09 | 96.34 | 97.14 | 98.44 | Wang, Y., Zhang, J., Wei, R., Gao, W., & Wang, Y. MFRGN: Multi-scale Feature Representation Generalization Network for Ground-to-Aerial Geo-localization. ACM MM2024. [[Code]](https://github.com/ytao-wang/MFRGN) |
 | BEV| 91.90 | 97.23 | 97.84 | 98.84 | Ye, J., Lv, Z., Li, W., Yu, J., Yang, H., Zhong, H., & He, C. (2024). Cross-view image geo-localization with Panorama-BEV Co-Retrieval Network. ECCV2024.|
 |*: The method utilizes the polar transformation (assuming that all satellite images face north) as input. | |
  |** : The method utilizes the polar prior hint. |