Self-Supervised Tuning for Few-Shot Segmentation
Authors: Kai Zhu, Wei Zhai, Yang Cao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark PASCAL-5i and COCO20i datasets demonstrate the superiority of our proposed method over state-of-the-art. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China {zkzy, wzhai056}@mail.ustc.edu.cn, forrest@ustc.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | To evaluate the performance of our model, we experiment on PASCAL-5i and COCO-20i datasets. |
| Dataset Splits | No | The paper describes train and test splits but does not explicitly mention a separate validation data split for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., library names with version numbers). |
| Experiment Setup | Yes | Our model uses the SGD optimizer during the training process. The initial learning rate is set to 0.0005 and the attenuation rate is set to 0.0005. The model stops training after 200 epochs. All images are resized to 321 321 size and the batch size is set to 16. |