MMA++: Effective Multi-Modal Adaptation for Vision-Language Models

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Large-scale Vision-Language Models (VLMs) exhibit strong generalization but struggle with few-shot adaptation.
• Adapting VLMs requires balancing general knowledge preservation with task-specific information integration.

Purpose of the Study:

• To propose MMA++, a Multi-Modal Adapter framework for efficient VLM adaptation in few-shot scenarios.
• To enhance few-shot generalization by optimizing adapter application and fusion scale dynamics.

Main Methods:

• MMA++ selectively applies adapters to higher layers of vision and text encoders based on feature analysis.
• A shared feature projection space is introduced to improve cross-modal alignment.
• An alpha-consistency framework dynamically adjusts the fusion scale (alpha) based on data size, using consistency training and alpha-decoupling.

Main Results:

• MMA++ demonstrates leading performance in few-shot generalization tasks, including base-to-novel generalization, cross-dataset transfer, and domain generalization.
• Empirical and theoretical analysis confirms that the fusion scale (alpha) should be adapted based on training data size.
• The alpha-consistency framework effectively reduces tuning effort across datasets.

Conclusions:

• MMA++ offers a parameter-efficient and effective approach for adapting large-scale VLMs to few-shot generalization.
• Dynamic adaptation of the fusion scale is crucial for optimal few-shot performance.
• The proposed framework significantly advances the capabilities of VLMs in low-data regimes.