Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
Authors: Xiaoyi Bao, Jie Qin, Siyang Sun, Xingang Wang, Yun Zheng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of our proposed method, which achieves state-of-the-art accuracy on both PASCAL-5i and COCO benchmarks. |
| Researcher Affiliation | Collaboration | 1School of Artificial Intelligence, University of Chinese Academy of Sciences 2Institute of Automation, Chinese Academy of Sciences 3Alibaba group |
| Pseudocode | No | The paper describes the model architecture and processes using text and figures but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on the PASCAL 5i and COCO datasets. |
| Dataset Splits | No | The paper describes how samples are drawn for meta-training tasks (e.g., 'K + 1 image pairs of the same class j'), but it does not specify explicit training/validation/test dataset splits (percentages or counts) for the overall PASCAL 5i or COCO datasets. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU models, CPU types, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions using 'Pre-trained Res Net50 and Res Net101' as backbones, but it does not provide specific version numbers for any software dependencies like deep learning frameworks (e.g., PyTorch, TensorFlow) or programming languages (e.g., Python). |
| Experiment Setup | Yes | The number of support: query: unlabeled images is 1 : 1 : 2 or 5 : 1 : 2 for each meta-training task in the one-shot and five-shot settings. The weight β is set to 0.5 empirically. We set m = 4 in the main experiments. Specific implementation details are presented in the Appendix. |