Title: Towards Adapting Vision-Language Models for Semi-Supervised Domain Generalization
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: domain generalization, semi-supervised learning

Abstract: Semi-supervised Domain Generalization (SSDG) offers a cost-effective solution for generalizing models to unseen domains with limited labels. While existing SSDG methods, mainly built upon small-scale backbones, struggle to match fully supervised DG performance, large-scale vision-language models like CLIP have shown remarkable generalization through downstream fine-tuning. However, adapting these models to SSDG remains underexplored. In this paper, we identify a critical issue: existing popular fine-tuning methods suffer from under-utilizing unlabeled data in the semi-supervised learning frameworks, thereby overfitting the limited labeled data, leading to training collapse and generalization ability degradation. To address these challenges, we propose two novel components: (1) the De-False-Correlation Adapter (DFC-Adapter), which reduces false correlations to refine visual features and (2) Learnable Multi-granularity Text-guided Embedding Augmentation (LMTEA), which synthesizes semantic-aligned but domain-perturbed augmented visual embedding for consistency regularization through multi-granularity text guidance and learnable style encoding. We establish the first benchmark for CLIP fine-tuning methods in SSDG, conducting extensive experiments across six DG datasets and two ImageNet variants. Our results demonstrate that our method significantly outperforms existing CLIP fine-tuning approaches and achieves performance comparable to fully supervised DG methods in some cases.