BHGraphAdapter: Parameter-Efficient VLMs Tuning Meets Hyper-Graph Learning

Adapter-based fine-tuning methods for Visual-Language Models (VLMs) have shown promising performance for feature adaptation in limited data scenarios. However, existing adapters generallyeitheremploy parameterized transformation for multi-modality feature refiningorexploit pairwise relationships between classes (i.e., GraphAdapter) for text enhancement, which ignore the inherent high-order correlations among data samples in the adaptation process. In this paper, for the first time, we propose to exploit the high-order relationships of visual samples within each mini-batch for fine-tuning VLMs and develop a novel Batch HyperGraph Adapter (BHGraphAdapter) to fine-tune VLMs. The core idea of BHGraphAdapter is to conduct feature adapter learning by capturing the inherent high-order semantic information of different samples within each mini-batch, which thus can fully exploit the complex context information in adaptation. Specifically, we first construct a Batch HyperGraph (BHGraph) to model the high-order correlation of samples within each mini-batch. Then, we introduce a message propagation module on BHGraph to update the node embeddings by aggregating information from their high-order neighbors, thereby capturing semantic relationships to enrich feature representation. Finally, we incorporate the proposed BHGraph learning into the pre-trained CLIP framework to achieve the feature adaptation for the downstream tasks. Extensive experiments on 11 benchmark datasets show that our proposed BHGraphAdapter outperforms the SOTA adapter tuning methods. The source code and data will be released at https://github.com/LiuMeilin7195/BHGraphAdapter.