SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation
Authors: Shi-Feng Peng, Guolei Sun, Yong Li, Hongsong Wang, Guo-Sen Xie
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four standard CD-FSS datasets demonstrate that our method establishes new state-of-the-art results. [...] Experiments, Datasets and Implementation Details, Comparisons with State-of-the-arts, Ablation Study and Visualizations |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China 2Computer Vision Laboratory, ETH Zurich 3School of Computer Science and Engineering, Southeast University, Nanjing, China EMAIL; EMAIL; EMAIL; EMAIL |
| Pseudocode | No | The paper describes the method conceptually and mathematically through equations but does not present a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/CVL-hub/GPRN. |
| Open Datasets | Yes | We fine-tune and test it on four datasets: Deepglobe (Demir et al. 2018), ISIC (Codella et al. 2019), Chest X-Ray (Candemir et al. 2013), and FSS-1000 (Li et al. 2020). |
| Dataset Splits | Yes | During fine-tuning, we used 120, 520, 60, and 20 samples from the Deepglobe, FSS-1000, ISIC, and Chest X-ray datasets, respectively. For evaluation, we randomly sample 600 episodes from ISIC, Deepglobe, and Chest X-Ray, and 2400 episodes from FSS-1000, averaging the results over five random seeds. |
| Hardware Specification | No | The paper mentions using a ResNet-50 backbone but does not provide specific details about the hardware (GPU/CPU models, memory, etc.) used for conducting experiments. |
| Software Dependencies | No | The paper mentions using SSP (Fan et al. 2022) and a ResNet-50 backbone, but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | All images are initially resized to 400x400 for input into the CNN and subsequently upsampled to 1024x1024 to meet SAM’s input requirements. The number of masks l is fixed as 40. ... The parameter α is set to 0.1. The numbers of positive and negative point prompts, |Z| and |N|, are both set to 20, and β is set to 0.5. During training, our model is optimized with 0.9 momentum and an initial learning rate of 1e-3 and training with 5 epochs. ... We employed the SGD optimizer with a learning rate of 5e-4, momentum of 0.9, and weight decay of 5e-4. |