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Prompt Learning

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Showing 551–575 of 678 papers

TitleStatusHype
Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge—0
Many-Objective Multi-Solution Transport—0
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation—0
SAM^Med: A medical image annotation framework based on large vision model—0
Enhancing Metaphor Detection through Soft Labels and Target Word Prediction—0
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts—0
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM—0
Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking—0
Mixture of Prompt Learning for Vision Language Models—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning—0
ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models—0
MoPD: Mixture-of-Prompts Distillation for Vision-Language Models—0
MorphSAM: Learning the Morphological Prompts from Atlases for Spine Image Segmentation—0
MoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval—0
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation—0
MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving—0
MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection—0
MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment—0
MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality—0
Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks—0
Multi-level Protein Structure Pre-training via Prompt Learning—0
Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout—0
Multimodal Prompt Alignment for Facial Expression Recognition—0
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels—0
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