Balanced Meta-Softmax for Long-Tailed Visual Recognition
Authors: Jiawei Ren, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, hongsheng Li
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks. |
| Researcher Affiliation | Collaboration | Jiawei Ren1, Cunjun Yu1, Shunan Sheng1,2, Xiao Ma1,3, Haiyu Zhao1*, Shuai Yi1, Hongsheng Li4 1 Sense Time Research 2 Nanyang Technological University 3 National University of Singapore 4 Multimedia Laboratory, The Chinese University of Hong Kong {renjiawei, zhaohaiyu, yishuai}@sensetime.com cunjun.yu@gmail.com shen0152@e.ntu.edu.sg xiao-ma@comp.nus.edu.sg hsli@ee.cuhk.edu.hk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks that are clearly labeled or formatted like code. |
| Open Source Code | Yes | Corresponding author Code available at https://github.com/jiawei-ren/Balanced Meta Softmax |
| Open Datasets | Yes | We perform experiments on long-tailed image classification datasets, including CIFAR-10-LT [18], CIFAR-100-LT [18], Image Net-LT [23] and Places-LT [34] and one long-tailed instance segmentation dataset, LVIS [7]. |
| Dataset Splits | Yes | For classification tasks, after training on the long-tailed dataset, we evaluate the models on the corresponding balanced test/validation dataset and report top-1 accuracy. We create the meta set by class-balanced sampling from the training set Dtrain. For LVIS, we use official training and testing splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | Our implementation details can be found in the supplementary materials. |