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..
Regularized Diffusion Process for Visual Retrieval
Authors: Song Bai, Xiang Bai, Qi Tian, Longin Jan Latecki
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the validity of the proposed Regularized Diffusion Process (RDP) with toy problems and real retrieval tasks. |
| Researcher Affiliation | Academia | Song Bai,1 Xiang Bai,1 Qi Tian,2 Longin Jan Latecki3 1Huazhong University of Science and Technology 2University of Texas at San Antonio, 3Temple University |
| Pseudocode | No | No pseudocode or algorithm block is explicitly presented in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology. |
| Open Datasets | Yes | Following the survey paper (Donoser and Bischof 2013), we assess the effectiveness of the proposed RDP on ORL face dataset, YALE face dataset B (Georghiades, Belhumeur, and Kriegman 2001) and MPEG-7 shape dataset (Latecki, Lak amper, and Eckhardt 2000). We also evaluate the proposed algorithm on the widely-used Ukbench dataset. |
| Dataset Splits | No | The paper describes using each image as a query and the rest as the database, but does not provide specific train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained Alex Net' but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | The regularizer μ in Eq. (1) is set to 0.18. ... we set A(1) randomly and the iteration number to 100. ... graph sparsification is applied by only preserving edges within k nearest neighbors. ... For Face and Shape Retrieval, k = 5, k is set to 10. For Natural Image Retrieval, k is set to 4. |