Meta-Adapter: An Online Few-shot Learner for Vision-Language Model (2311.03774v2)

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.

• The paper introduces Meta-Adapter, which enhances few-shot learning by integrating an online residual-style adapter into CLIP.
• It employs a meta-learning approach with gated multi-head attention to refine textual category embeddings using minimal image samples.
• Experimental results reveal a 3.6% average accuracy boost and improved generalization across multiple vision-language benchmarks.

Summary of "Meta-Adapter: An Online Few-shot Learner for Vision-LLM"

Introduction

The paper introduces the Meta-Adapter, a novel approach designed to enhance the few-shot learning capabilities of CLIP (Contrastive Language-Image Pre-training) models in the context of vision-language tasks. The main motivation behind this work is to address the limitations present in existing few-shot learning methods, which often lead to increased inference times and potential overfitting when domains differ significantly. Meta-Adapter aims to refine CLIP features using an online residual-style adapter, facilitating efficient learning from minimal data while improving generalization to unseen data and tasks. This approach achieves notable performance improvements across multiple benchmark datasets.

Methodology

CLIP and Few-shot Learning Challenges

The vision-language pre-training paradigm, especially CLIP, demonstrates significant capabilities in handling zero-shot image classification tasks through contrastive learning using large-scale datasets. However, integrating few-shot learning into this paradigm often involves offline fine-tuning, which can be computationally expensive and susceptible to overfitting, particularly in diverse domains.

Meta-Adapter Design

The Meta-Adapter is proposed as a lightweight, residual-style network that operates in conjunction with the CLIP framework, enhancing its few-shot learning capabilities without additional offline fine-tuning. The method employs a meta-learning approach to refine textual category embeddings using few-shot image samples, leveraging gated multi-head attention to integrate image-derived features subtly and effectively with pre-existing textual representations. This configuration allows Meta-Adapter to function as a plug-and-play module, extendable to various vision-language tasks, including open-vocabulary object detection and segmentation.

Experimental Evaluation

Performance Metrics

Meta-Adapter outperforms state-of-the-art few-shot learning methods, demonstrated by an average increase in accuracy of 3.6% across eight image classification datasets. It maintains higher inference speeds compared to its counterparts. The methodology yields consistent performance improvements in open-vocabulary object detection and segmentation tasks without further fine-tuning.

Robustness and Generalization

Cross-category and cross-dataset generalization studies highlight the robustness of Meta-Adapter, showcasing increased adaptability to diverse data distributions. Moreover, the model's generic nature facilitates transferability to different datasets without significant loss of efficacy, illustrated by relative improvements over Tip-Adapter and Zero-shot CLIP approaches.

Related Work

The paper situates the development of Meta-Adapter within current research on vision-language pre-trained models and few-shot learning strategies. It builds upon the foundational principles of CLIP, while diverging from traditional offline tuning methods by using meta-learning techniques. This distinction ensures wider applicability and enhanced generalization capacity across contexts, marking a departure from earlier models that rely heavily on dataset-specific tuning processes.

Conclusion

Meta-Adapter addresses key challenges in the field of few-shot learning within vision-LLMs, specifically targeting computational efficiency and generalization concerns associated with task variability. This work suggests potential avenues for integrating similar techniques into further vision tasks, unlocking new capabilities in the field of AI-enhanced vision-language modeling. Future research could investigate the extension of Meta-Adapter methodologies to broader applications in AI, particularly those involving complex domain shifts and multimodal integration.