Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Authors: Jintao Tong, Ran Ma, Yixiong Zou, Guangyao Chen, Yuhua Li, Ruixuan Li
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method surpasses the state-of-the-art method in CD-FSS significantly by 2.69% and 4.68% MIo U in 1-shot and 5-shot scenarios, respectively. Section 4. Experiments |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Huazhong University of Science and Technology 2Peking University. Correspondence to: BYixiong Zou <EMAIL>. |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical formulations (e.g., equations 1-9) rather than structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement offering access to source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We employ PASCAL (Shaban et al., 2017) , which is an extended version of PASCAL VOC 2012 (Everingham et al., 2010), as our source-domain dataset for training. We regard FSS-1000 (Li et al., 2020), Deepglobe (Demir et al., 2018), ISIC2018 (Codella et al., 2019; Tschandl et al., 2018), and Chest X-ray (Candemir et al., 2013; Jaeger et al., 2013) as target domains for evaluation. |
| Dataset Splits | Yes | We employ PASCAL (Shaban et al., 2017) as our source-domain dataset for training... FSS-1000 (Li et al., 2020): We follow the official split for semantic segmentation in our experiment and present results on the designated testing set, consisting of 240 classes and 2,400 images... ISIC2018 (Codella et al., 2019; Tschandl et al., 2018): The dataset is processed and utilized in accordance with the standards set by PATNet. |
| Hardware Specification | Yes | We use a single 4090 GPU for training and testing. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and models like ResNet-50 and ViT, but does not provide specific version numbers for any software libraries, programming languages, or frameworks. |
| Experiment Setup | Yes | The model is trained using the Adam (Min et al., 2021) optimizer with a learning rate of 1e-3. The hyperparameter ρ in SAM is set to 0.5... The fine-tuning of DFN is performed using the Adam optimizer, with learning rates set at 1e-3 for FSS-1000, 5e-1 for Deepglobe, 5e-3 for ISIC and Chest X-ray. Each task undergoes a total of 50 iterations... we set the spatial sizes of both support and query images to 400 400. |