Title: UPrompt: Bidirectional Multi-granularity Learning for Vision-Language Models
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: prompt learning, multi-granularity learning, vision-language models

Abstract: The prompt learning paradigm for vision-language models is effective yet faces the dilemma of balancing granularity: global prompts lack fine-grained semantic awareness, while local prompts ignore overall contextual associations, leading to limited cross task generalization. This dilemma exists in dense prediction tasks.
Inspired by the U-Net framework that unifying multi-level representations across different granularities, we propose UPrompt, a novel bidirectional multi-granularity prompt learning framework for vision-language models.
Similar to how U-Net integrates fine and coarse features through symmetric encoder-decoder pathways with cross-level connections, UPrompt constructs parallel multi-granularity representations in both visual and textual modalities, where coarse-to-fine cascaded enhancement propagates global contextual information to refine local details, while fine-to-coarse hierarchical supervision ensures semantic consistency across scales. 
Extensive experiments on 17 benchmarks validate our effectiveness. Our method outperforms MAMET and VPKE by +4.1 and +7.3 rSum on MSCOCO, surpasses CoCoA-Mix by +5.09\% in base-to-novel generalization, while maintaining competitive performance with minimal overhead (coarse-grained) and matching PSRC with 1/3 cost (medium-grained).