HISF: Hierarchical Interactive Semantic Fusion for Multi-Modal Prompt Learning

Recent vision-language pre-training models, like CLIP, have been shown to generalize well across a variety of multitask modalities. Nonetheless, their generalization for downstream tasks is limited. As a light-weight adaptation approach, prompt learning could allow task transfer by optimizing only several learnable vectors, and thus is more flexible for pre-trained models. However, current methods mainly concentrate on the design of unimodal prompts and ignore effective means for multimodal semantic fusion and label alignment, which limits their representation power. To tackle these problems, this paper designs a Hierarchical Interactive Semantic Fusion (HISF) framework for multimodal prompt learning. On top of frozen CLIP backbones, HISF injects visual and textual signals simultaneously in intermediate layers of a Transformer through a cross-attention mechanism as well as fitting category embeddings. This architecture realizes the hierarchical semantic fusion at the modality level with structural consistency kept at each layer. In addition, a Label Embedding Constraint and a Semantic Alignment Loss are proposed to promote category consistency while alleviating semantic drift in training. Extensive experiments across 11 few-shot image classification benchmarks show that HISF improves the average accuracy by around 0.7% compared to state-of-the-art methods and has remarkable robustness in cross-domain transfer tasks. Ablation studies also verify the effectiveness of each proposed part and their combination: hierarchical structure, cross-modal attention, and semantic alignment collaborate to enrich representational capacity. In conclusion, the proposed HISF is a new hierarchical view for multimodal prompt learning and provides a more lightweight and generalizable paradigm for adapting vision-language pre-trained models.