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..
Topological RANSAC for instance verification and retrieval without fine-tuning
Authors: Guoyuan An, Ju-hyeong Seon, Inkyu An, Yuchi Huo, Sung-eui Yoon
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that our method significantly outperforms SP, achieving stateof-the-art performance in non-fine-tuning retrieval. Furthermore, our approach can enhance performance when used in conjunction with fine-tuned features. |
| Researcher Affiliation | Academia | Guoyuan An1, Juhyung Seon1, In Kyu An1,4, Yuchi Huo2,3, and Sung-Eui Yoon1 1School of Computing, KAIST 2 State Key Lab of CAD&CG, Zhejiang University 3Zhejiang Lab 4ETRI, Electronics and Telecommunications Research Institute |
| Pseudocode | Yes | Algorithm 1 shows the overall pipeline of our method. |
| Open Source Code | Yes | Our code can be found through this link. |
| Open Datasets | Yes | Table 1: Results (% m AP) on the ROxf/RPar datasets and their large-scale versions ROxf+1M/RPar+1M, with both Medium and Hard evaluation protocols. |
| Dataset Splits | No | The paper refers to datasets like ROxford and RParis for evaluation and mentions 'non-fine-tuning retrieval' scenarios, but it does not explicitly provide training/test/validation dataset splits with specific percentages or sample counts for its experiments. It also discusses fine-tuning on GLD but doesn't detail its own splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments, such as GPU or CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions its implementation as 'Python-based method' and compares its speed to 'C-implemented SP', but it does not specify any particular software dependencies with version numbers (e.g., specific libraries, frameworks, or solvers). |
| Experiment Setup | Yes | The paper provides some specific experimental setup details, such as: 'The threshold α is set as 0.2.' and 'For fairness, all methods rerank the top 100.' |