Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning
Authors: Wenhao Li, Qiangchang Wang, Xianjing Meng, Zhibin Wu, Yilong Yin
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 33,552 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,079 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted in three distinct few-shot learning scenarios. |
| Researcher Affiliation | Academia | 1School of Software, Shandong University 2Shenzhen Loop Area Institute 3School of Computing and Artificial Intelligence, Shandong University of Finance and Economics |
| Pseudocode | Yes | A.1 Training Algorithm Algorithm 1: Training algorithm of the proposed VT-FSL. |
| Open Source Code | Yes | Code is available at https://github.com/peacelwh/VT-FSL. |
| Open Datasets | Yes | Datasets. Extensive experiments are conducted in three distinct few-shot learning scenarios. (1) Four datasets in standard FSL: mini Image Net [12], tiered Tiered Net [60], CIFAR-FS [67], and FC100 [19]. (2)Three datasets in fine-grained FSL: CUB [68], Cars [69], and Dogs [70]. (3)Three datasets in cross-domain FSL: CUB, Places [71], and Plantae [72]. |
| Dataset Splits | Yes | Table 8: The splits of categories and the number of categories/images in each few-shot dataset. ... mini Image Net [12] 64 16 20 12,000 60,000 |
| Hardware Specification | Yes | All experiments are performed with an NVIDIA RTX 6000 Ada. |
| Software Dependencies | No | The paper mentions specific models (Visformer Tiny, CLIP, Qwen2.5-VL-32B, Janus-Pro) and an optimizer (Adam W), but does not list specific version numbers for general software dependencies such as Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | The Adam W optimizer [73] is used with a learning rate of 5e-4 and a cosine scheduler. Pre-training runs for 300 epochs in tiered Image Net and 800 epochs in other datasets with a batch size of 512, followed by meta-tuning for 100 epochs via an episodic training strategy. The hyperparameter τ is set as 0.2 according to validation accuracy. |