Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Authors: cheng cheng, Lin Song, Ruoyi Xue, Hang Wang, Hongbin Sun, Yixiao Ge, Ying Shan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments demonstrate the effectiveness and efficiency of the Meta-Adapter on image classification, object detection, and segmentation. To verify the generalizability of Meta-Adapter, we conduct a series of ablation studies, including cross-category generalization within a certain dataset, cross-dataset generalization, and cross-task generalization which explores the potential of Meta-Adapter in downstream tasks. |
| Researcher Affiliation | Collaboration | 1 Xi an Jiao Tong University 2Tencent AI Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it include an explicit code release statement or repository link. |
| Open Datasets | Yes | For cross-category generalization experiments, we use 8 representative image classification datasets: Image Net [29], FGVCAircraft [51], Oxford Pets [52], SUN397 [53], UCF101 [54], Caltech101 [55], DTD [56], and Euro SAT [57]... As for the cross-dataset generalization experiment, Image Net is further utilized as the source dataset and its three variants are treated as target datasets, i.e., Image Net-A [58], Image Net-R [59], and Image Net-Sketch [60]. Moreover, to explore the potential of Meta-Adapter on open-vocabulary detection, we conduct experiments on LVIS [61]. |
| Dataset Splits | Yes | Specifically, for cross-category generalization , we split the full categories of each dataset into base and novel sets according to the per-category accuracy predicted by Zero-shot CLIP, that is, the base set contains easy samples and the novel set contains hard samples. This dataset split strategy simulates a rather difficult situation to verify whether Meta-Adapter is able to learn the dataset-irrelevant approach, especially for hard samples. |
| Hardware Specification | Yes | The time is measured on a Tesla V100 GPU. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We optimize the Meta-Adapter on the base set with a batch size of 64 and use Adam W optimizer [62] with a learning rate of 0.0001 and a cosine scheduler for 5 epochs. |