Suppressing the Heterogeneity: A Strong Feature Extractor for Few-shot Segmentation
Authors: Zhengdong Hu, Yifan Sun, Yi Yang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Collaboration | 1 Re LER, Centre for Artificial Intelligence, University of Technology Sydney, Australia 2 Baidu Inc. 3 CCAI, College of Computer Science and Technology, Zhejiang University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states "The Mu HS is reproducible" and describes implementation details, but does not provide an explicit statement about releasing open-source code or a link to a repository. |
| Open Datasets | Yes | We evaluate the proposed Mu HS on two datasets: PASCAL-5i (Shaban et al. (2017)) and COCO20i (Nguyen & Todorovic (2019)). |
| Dataset Splits | Yes | We divide 20 classes into 4 splits and each split has 5 classes. During evaluation on one split (5 classes), we have other three splits (15 classes) for training. We randomly sample 1000 pairs of support and query in each split testing. |
| Hardware Specification | Yes | The proposed Mu HS is trained on Pytorch with 4 NVIDIA A100 GPUS. |
| Software Dependencies | No | The paper mentions "Pytorch" but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We use SGD optimizer and set the learning rate as 9e-4. We randomly crop images to 480 480 and follow the data augmentation in PFENet (Tian et al. (2020b)). For PASCAL-5i, we train 50 epochs with batch size 4. For COCO-20i, we train 30 epochs and set batch size to 16. |