Region Mutual Information Loss for Semantic Segmentation
Authors: Shuai Zhao, Yang Wang, Zheng Yang, Deng Cai
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |