U-shaped Multi-granularity Learning for Vision-Language Models
The prompt learning paradigm for vision-language models is effective yet faces a granularity dilemma: global prompts lack fine-grained semantic awareness, while local prompts ignore contextual associations, limiting cross-task generalization. This dilemma exists in dense prediction tasks. Inspired by U-Net, which unifies multi-level representations across granularities, we propose UPrompt, a U-shaped 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 connect
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “U-shaped Multi-granularity Learning for Vision-Language Mode” ≈ “VioletVision-3B””
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “U-shaped Multi-granularity Learning for Vision-Language Mode” ≈ “pytorch/vision””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “U-shaped Multi-granularity Learning for Vision-Language Mode” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Biao Chen →
“U-shaped Multi-granularity Learning for Vision-Language Models”
- LinkedLinked via arxiv author · 85%Yunqian Yu →
“U-shaped Multi-granularity Learning for Vision-Language Models”
- LinkedLinked via arxiv author · 85%Xiangxu Zhao →
“U-shaped Multi-granularity Learning for Vision-Language Models”
- LinkedLinked via arxiv author · 85%Zhongshu Chen →
“U-shaped Multi-granularity Learning for Vision-Language Models”
- LinkedLinked via arxiv author · 85%Mengmeng Jing →
“U-shaped Multi-granularity Learning for Vision-Language Models”