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 | Conference PDF | Archive PDF | Plain Text | 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.' |