Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

• URL: http://arxiv.org/abs/2311.03774v2
• Date: Thu, 11 Jan 2024 06:03:56 GMT
• Title: 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
• Abstract summary: Contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts. Few-shot learning methods based on CLIP typically require offline fine-tuning of the parameters on few-shot samples. 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.
• Score: 64.21017759533474
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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