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cs.CV2024
Panther: Illuminate the Sight of Multimodal LLMs with Instruction-Guided Visual Prompts
Honglin Li, Yuting Gao, Chenglu Zhu +3
Multimodal large language models (MLLMs) are closing the gap to human visual perception capability rapidly, while, still lag behind on attending to subtle images details or locatin…
cs.CV2024
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Wensheng Pan, Timin Gao, Yan Zhang +10
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…
cs.CV2024
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Yuncheng Yang, Chuyan Zhang, Zuopeng Yang +6
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently opt…