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..
M-Best-Diverse Labelings for Submodular Energies and Beyond
Authors: Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental evaluation, Quantitative comparison and run-time of the considered methods is provided in Table 1, PASCAL VOC 2012 challenge [9] contains 1449 validation images with known ground truth, which we used for evaluation of diversity methods. |
| Researcher Affiliation | Academia | 1 TU Dresden, Dresden, Germany 2 Skoltech, Moscow, Russia |
| Pseudocode | No | No clearly labeled pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide explicit statements about releasing their source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | Category level segmentation on PASCAL VOC 2012 data [9] |
| Dataset Splits | No | The PASCAL VOC 2012 challenge [9] contains 1449 validation images with known ground truth, which we used for evaluation of diversity methods. optimally tuned via cross-validation on the validation set in PASCAL VOC 2012. It mentions specific validation data but not explicit splits for train/validation/test from a larger dataset or the standard splits for PASCAL VOC 2012, only that they used the validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions software components and algorithms like 'min-cut/max-flow problem [21, 28, 16]', 'solver [5]', 'α-expansion [6]', 'α-β-swap [6]' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | each method was used with the parameter λ (see (2), (4)), optimally tuned via cross-validation. |