M-Best-Diverse Labelings for Submodular Energies and Beyond
Authors: Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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. |