Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
Authors: Mushui Liu, Fangtai Wu, Bozheng Li, Ziqian Lu, Yunlong Yu, Xi Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four few-shot classification benchmarks and the BSCD-FSL cross-domain benchmark showcase remarkable advancements over the current state-of-the-art methods. Notably, for the challenging one-shot setting, our approach, utilizing the Res Net-12 backbone, achieves an impressive average improvement of 1.95% over the second-best competitor. |
| Researcher Affiliation | Academia | 1College of Information Science & Electronic Engineering, Zhejiang University 2School of Aeronautics and Astronautics, Zhejiang University 3College of Computer Science and Technology, Zhejiang University EMAIL |
| Pseudocode | No | The paper describes its methodology and formulations using natural language and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | Yes | We evaluate the proposed method across two primary tasks: the traditional FSL and the cross-domain FSL (CD-FSL). The traditional FSL is evaluated on four datasets, namely Mini Image Net (Vinyals et al. 2016), Tiered Image Net (Ren et al. 2018), CIFAR-FS (Lee et al. 2019), and FC100 (Oreshkin, Rodr ıguez L opez, and Lacoste 2018). Following (Guo et al. 2020), we evaluate the CD-FSL on BSCD-FSL benchmark, which involves training on Mini Image Net and testing on four unrelated datasets: Chest X (Wang et al. 2017), ISIC (Tschandl, Rosendahl, and Kittler 2018), Euro SAT (Helber et al. 2019), and Crop Disease (Mohanty, Hughes, and Salath e 2016). |
| Dataset Splits | Yes | For the evaluation, we uniformly sampled 600 classification tasks from a novel set that comprises classes that are disjoint from those in the base set. In each task, there are 15 query samples for each class. The mean and 95% confidence interval of the accuracy are reported. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Vi T-B/32 CLIP (Radford et al. 2021)' and 'GPT-4-o (Open AI 2023)' as models/APIs, but it does not specify any general software dependencies or library versions needed for reproducibility (e.g., Python version, PyTorch version). |
| Experiment Setup | Yes | The pre-training stage is set to 200 epochs for all datasets, while the meta-training stage is set to 50 epochs. The α and β in Eq. (9) are consistently assigned values of 0.2 and 0.8, respectively, across all datasets. During the pre-training phase, we set the batch size to 128, leveraging the Adam optimizer (Kingma and Ba 2014) with a learning rate of 1e-4 for optimization of the model parameters. |