DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models
This addresses the challenge of unsupervised domain adaptation for vision-language models, which is crucial for deploying these models in real-world scenarios where labeled data is scarce, though it is incremental as it builds on existing pseudo-labeling methods.
The paper tackled the problem of adapting vision-language models like CLIP to new domains without labeled data by proposing DPA, which uses dual prototypes and alignment to reduce pseudo-label noise, resulting in significant performance gains over zero-shot CLIP and state-of-the-art baselines across 13 vision tasks.
Vision-language models (VLMs), e.g., CLIP, have shown remarkable potential in zero-shot image classification. However, adapting these models to new domains remains challenging, especially in unsupervised settings where labeled data is unavailable. Recent research has proposed pseudo-labeling approaches to adapt CLIP in an unsupervised manner using unlabeled target data. Nonetheless, these methods struggle due to noisy pseudo-labels resulting from the misalignment between CLIP's visual and textual representations. This study introduces DPA, an unsupervised domain adaptation method for VLMs. DPA introduces the concept of dual prototypes, acting as distinct classifiers, along with the convex combination of their outputs, thereby leading to accurate pseudo-label construction. Next, it ranks pseudo-labels to facilitate robust self-training, particularly during early training. Finally, it addresses visual-textual misalignment by aligning textual prototypes with image prototypes to further improve the adaptation performance. Experiments on 13 downstream vision tasks demonstrate that DPA significantly outperforms zero-shot CLIP and the state-of-the-art unsupervised adaptation baselines.