Variational Adapter: Improving CLIP in Data-Imbalanced Scenarios

In this paper, we propose the Prompt-based Variational Adapter (PVA), a novel approach designed to fine-tune the pre-trained Vision-Language Models (VLMs) in data-imbalanced scenarios. Unlike existing methods that focus primarily on pairwise alignment of visual-text relationships during fine-tuning, PVA relaxes pairwise explicit constrains and emphasizes the harmonization of visual and text modality distributions, enhancing generalization and cross-modal understanding. To realize this harmonization, we develop two variational adapters, which are appended separately to the visual and text encoders. These adapters transform the feature embeddings into latent spaces that implicitly align with the corresponding modality distributions. We then adopt a divide-and-conquer strategy, dividing classes into data-abundant and data-limited sets to reduce prediction bias. Within each set, we independently fine-tune the models by incorporating both the model’s original general knowledge and specialized knowledge gained from training samples. Extensive experiments across two data-imbalanced scenarios validate the superiority of our approach, establishing a new state-of-the-art on popular benchmarks.