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..
Region Mutual Information Loss for Semantic Segmentation
Authors: Shuai Zhao, Yang Wang, Zheng Yang, Deng Cai
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that RMI can achieve substantial and consistent improvements in performance on PASCAL VOC 2012 and Cam Vid datasets. |
| Researcher Affiliation | Collaboration | 1State Key Lab of CAD&CG, College of Computer Science, Zhejiang University 2School of Artificial Intelligence and Automation, Huazhong University of Science and Technology 3Fabu Inc., Hangzhou, China 4Alibaba-Zhejiang University Joint Institute of Frontier Technologies |
| Pseudocode | No | The paper describes the methodology mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/ZJULearning/RMI. |
| Open Datasets | Yes | We evaluate our method on two dataset, PASCAL VOC 2012 [10] and Cam Vid [4] datasets. |
| Dataset Splits | Yes | PASCAL VOC 2012 dataset contains 1 464 (train), 1 449 (val), and 1 456 (test) images. |
| Hardware Specification | No | The paper mentions 'GPU memory' but does not provide specific hardware details such as GPU model, CPU, or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch [26] and Tensorflow [1]' as supported platforms but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For PASCAL VOC 2012 dataset, the model is trained on the trainaug [13] set which contains 10 582 images, max_iter is about 30K, lr = 0.007, and slow_iters = 1.5K. For Cam Vid dataset, we train the model on the train and validation sets, max_iter is about 6K, lr = 0.025, and slow_iters = 300. During training, the batch size is always 16. The crop size is 513 and 479 for PASCAL VOC 2012 and Cam Vid datasets respectively. |