Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
Authors: Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on standard benchmark datasets show significant performance boosting, achieving more than doubled recall rate compared with the previous state of the art. |
| Researcher Affiliation | Academia | Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li Australian National University, Canberra, Australia. Australian Centre for Robotic Vision, Australia. {firstname.lastname}@anu.edu.au |
| Pseudocode | No | The paper describes its methods in narrative text and uses diagrams (Figure 2, Figure 3) but does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The code of this paper is available at https://github.com/shiyujiao/SAFA. |
| Open Datasets | Yes | Our experiments are conducted on two standard benchmark datasets: CVUSA [24] and CVACT [11], where ground images are panoramas. |
| Dataset Splits | Yes | CVUSA and CVACT are both cross-view datasets, and each dataset contains 35, 532 ground-and-aerial image pairs for training. CVUSA provides 8, 884 image pairs for testing and CVACT provides the same number of pairs for validation (denoted as CVACT_val). Besides, CVACT also provides 92, 802 crossview image pairs with accurate Geo-tags to evaluate Geo-localization performance (denoted as CVACT_test). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only mentions a 'GPU gift donated by NVIDIA Corporation' in the acknowledgements, but not for the experimental setup. |
| Software Dependencies | No | The paper mentions using a 'VGG16 model' and 'Adam optimizer [7]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Similar to [5, 11], we set γ to 10 for the triplet loss. Our network is trained with Adam optimizer [7], and the learning rate is set to 10 5. Exhaustive mini-batch strategy [19] is utilized to create triplet images within a batch, and the batch size Bs is set to 32. |