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

Large scale pre-trained Vision-Language Models (VLMs) have shown good generalization capabilities across diverse downstream tasks. However, adapting such large-scale models to few-shot generalization scenarios remains challenging due to the trade-off between preserving general knowledge and incorporating task-specific information. In this paper, we propose MMA++, an advanced and effective Multi-Modal Adapter framework for parameter-efficient VLM adaptation. Unlike prior works that independently inject adapters into each modality or uniformly across layers, MMA++ performs a dataset-level analysis to identify discriminative and generalizable features, and selectively applies adapters to the higher layers of both vision and text encoders. To bridge the modality gap, we further propose a shared feature projection space that enhances alignment between modalities. Beyond architecture design, we identify the fusion scale $\alpha$—which controls the strength of adapter integration—as a key factor in few-shot generalization. We empirically and theoretically demonstrate that $\alpha$ should not be static, but adapted based on training data size. To reduce the effort of tuning this value across different datasets, we propose the $\alpha$-consistency framework, consisting of: (1) a consistency training strategy under varying fusion scales; and (2) an $\alpha$-decoupling strategy that uses a larger fusion scale during training and a smaller one at inference to account for sample size mismatch. We evaluate MMA++ on a wide range of few-shot generalization tasks, including base-to-novel generalization, cross-dataset transfer, and domain generalization. Our method consistently achieves leading performance.