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..
Suppressing the Heterogeneity: A Strong Feature Extractor for Few-shot Segmentation
Authors: Zhengdong Hu, Yifan Sun, Yi Yang
ICLR 2023 | Venue PDF | 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. |