Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
Authors: Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li
NeurIPS 2019 | Venue PDF | 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. |