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..
FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation
Authors: Yuxuan Zhang, Wei Yang, Shaowei Wang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that FGNet reduces both the gaps for FSS by SAM and IFSM respectively, and achieves stateof-the-art performances on both PASCAL-5i and COCO-20i datasets compared with previous topperforming approaches. |
| Researcher Affiliation | Academia | Yuxuan Zhang1 , Wei Yang1,2,3, , Shaowei Wang4 1 School of Computer Science and Technology, University of Science and Technology of China 2Suzhou Institute for Advanced Research, University of Science and Technology of China 3Hefei National Laboratory 4 Institute of Artificial Intelligence and Blockchain, Guangzhou University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods in text and uses diagrams (e.g., Figure 2 and 3) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: github.com/YXZhang979/FGNet |
| Open Datasets | Yes | To evaluate the performance of FGNet, we conduct experiments on two widely-used FSS datasets, i.e., PASCAL-5i [Shaban et al., 2017] and COCO-20i [Lin et al., 2014]. |
| Dataset Splits | Yes | The categories are partitioned into four equal splits for crossvalidation. Specifically, three splits are selected for training, while the rest is for evaluation. |
| Hardware Specification | No | The paper mentions using a ResNet backbone but does not provide specific hardware details (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using SGD optimizer, but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA) needed to replicate the experiment. |
| Experiment Setup | Yes | We use SGD optimizer to train FGNet, with 0.9 momentum and 5e-3 initial learning rate. To separate different classes, we set a large batch size of 16. All images are cropped to 473 473 resolution and augmented by random horizontal flipping. |