Task-Adaptive Prompted Transformer for Cross-Domain Few-Shot Learning
Authors: Jiamin Wu, Xin Liu, Xiaotian Yin, Tianzhu Zhang, Yongdong Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on the Meta-Dataset benchmark demonstrate that our method achieves superior results against state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Deep Space Exploration Laboratory/School of Information Science and Technology, University of Science and Technology of China |
| Pseudocode | No | The paper describes methods in text and uses diagrams (Figure 1, Figure 2) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link (https://github.com/hushell/pmf cvpr22) to the official code of a *baseline* method (PMF), but does not provide a link or statement about the availability of the code for their own proposed Meta Prompt method. |
| Open Datasets | Yes | We evaluate our model on Meta-Dataset (Triantafillou et al. 2019), a cross-domain few-shot learning benchmark that collects 10 public image datasets from a diverse range of domains: Im Net, Omni, Acraft, Bird, DTD, QDraw, Fungi, Flwr, Sign and COCO. |
| Dataset Splits | Yes | We use the first 8 datasets for meta-training, where each dataset is further divided into train/val/test splits with disjoint classes. The test split of these datasets is used to evaluate the performance of seen domains (in-domain performance). |
| Hardware Specification | No | The paper specifies model architectures used (Vi T-S, Vi T-B) and pre-trained weights, but does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adadelta) but does not provide specific version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | The length of the prompt is set as 8. We follow the episodic training protocol and use the SGD optimizer with a learning rate of 1e 4 for the Vi T backbone and 5e 4 for the prompt generator. The task-adaptive prompt is inserted into the second layer of Vi T, which we empirically found performs best. During meta-test, we randomly sample 600 N-way K-shot tasks from the meta-test split of each dataset, where N varies from 5 to 50 and K varies from 1 to 100. The bias parameters of the backbone are tuned for 30 iterations for each task, using the Adadelta optimizer with a learning rate of 1. |