  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.
