APoLLo: Unified Adapter and Prompt Learning for Vision Language Models
This addresses the challenge of enhancing few-shot adaptation for vision-language models, which is incremental as it builds on existing adapter and prompt techniques.
The paper tackles the problem of improving generalization in Vision-Language Pretrained models under few-shot settings by introducing APoLLo, a unified method combining adapter and prompt learning, which achieves up to 6.03% relative gain over state-of-the-art methods on novel classes across 10 datasets.
The choice of input text prompt plays a critical role in the performance of Vision-Language Pretrained (VLP) models such as CLIP. We present APoLLo, a unified multi-modal approach that combines Adapter and Prompt learning for Vision-Language models. Our method is designed to substantially improve the generalization capabilities of VLP models when they are fine-tuned in a few-shot setting. We introduce trainable cross-attention-based adapter layers in conjunction with vision and language encoders to strengthen the alignment between the two modalities. We enforce consistency between the respective encoder branches (receiving augmented inputs) to prevent overfitting in downstream tasks. Our method is evaluated on three representative tasks: generalization to novel classes, cross-dataset evaluation, and unseen domain shifts. In practice, APoLLo achieves a relative gain up to 6.03% over MaPLe (SOTA) on novel classes for 10 diverse image recognition datasets.