Fine-Tuning via Linked Domains: A Closed-Form Dual Alignment Mechanism for Transferring Vision-Language Models

Adapters and prompt learning have become two de facto strategies to fine-tune pre-trained vision-language models, mitigating the high computational cost of fine-tuning an entire model for downstream tasks. They can align the prediction from the fine-tuned model with that from the pre-trained model. However, the existing methods of these strategies primarily focus on aligning within a single modality, and the exploration of bidirectional interactions between modalities remains limited. To address this issue, we propose a closed-form dual alignment mechanism (DAM) that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not only ensures the consistency in predictions within a single modality but also achieves the alignment of features across different modalities</i>. In DAM, all alignments are achieved by closed-form solutions to ridge regression, without inducing a massive number of learnable parameters. Experimental results demonstrate that DAM outperforms the state-of-the-art methods on 11 benchmarks over various evaluation metrics. Our codes are available at https://github.com/Peiy-Lu/DAM.