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Prompt Learning

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Showing 501–525 of 678 papers

TitleStatusHype
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
Exploration of visual prompt in Grounded pre-trained open-set detection—0
Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning—0
Domain Prompt Learning with Quaternion Networks—0
OpenSD: Unified Open-Vocabulary Segmentation and DetectionCode0
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited ScenariosCode0
Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting—0
Adaptive Multi-Modality Prompt Learning—0
DAP: Domain-aware Prompt Learning for Vision-and-Language Navigation—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing—0
Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection?Code0
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models—0
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot LearningCode0
CLIP Guided Image-perceptive Prompt Learning for Image Enhancement—0
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization—0
Unleashing the potential of prompt engineering for large language models—0
Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment PredictionCode0
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation—0
AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models—0
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