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 [1].
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning
Authors: Ran Ma, Yixiong Zou, Yuhua Li, Ruixuan Li
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four CDFSL datasets demonstrate that our method achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Huazhong University of Science and Technology EMAIL |
| Pseudocode | No | The paper describes the methodology using text and diagrams (Figure 6 and 7) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Following the BSCD benchmark (Guo et al. 2020), we use mini Imagenet (Vinyals et al. 2016) as the source dataset and four cross-domain datasets as target datasets: Crop Disease (Mohanty, Hughes, and Salath e 2016), Euro SAT(Helber et al. 2019), ISIC2018 (Codella et al. 2018) and Chest X (Wang et al. 2017). |
| Dataset Splits | Yes | Mini Image Net has 100 natural image categories with 600 images each, split into 64 training, 16 validation, and 20 test categories. ... existing research (Snell, Swersky, and Zemel 2017; Guo et al. 2020) employ a k-way n-shot paradigm to sample from DT , forming small datasets (episodes) containing k classes with n training samples each. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | During base class training, we train the model with Adam W (Loshchilov and Hutter 2019) with a learning rate of 0.001 for the classifier, 1e-7 for the backbone, and 1e-6 for the decoder. For novel-class fine-tuning, we discard the decoder and fine-tune the backbone using the SGD optimizer with a momentum of 0.9. |