The contrastive vision-language pre-training, known as CLIP, demonstrates
remarkable potential in perceiving open-world visual concepts, enabling
effective zero-shot image recognition. Nevertheless, few-shot learning methods
based on CLIP typically require offline fine-tuning of the parameters on
few-shot samples, resulting in longer inference time and the risk of
over-fitting in certain domains. To tackle these challenges, we propose the
Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features
guided by the few-shot samples in an online manner. With a few training
samples, our method can enable effective few-shot learning capabilities and
generalize to unseen data or tasks without additional fine-tuning, achieving
competitive performance and high efficiency. Without bells and whistles, our
approach outperforms the state-of-the-art online few-shot learning method by an
average of 3.6\% on eight image classification datasets with higher inference
speed. Furthermore, our model is simple and flexible, serving as a
plug-and-play module directly applicable to downstream tasks. Without further
fine-tuning, Meta-Adapter obtains notable performance improvements in
open-vocabulary object detection and segmentation tasks.

提出了 Meta-Adapter 方法，它是一种轻量级的残差式适配器，通过在线学习利用少量样本来优化 CLIP 特征，从而实现有效的少样本学习能力和泛化到未见过的数据或任务的能力，并取得了竞争性的性能和高效率。