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Prompt Learning

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Showing 326–350 of 678 papers

TitleStatusHype
Instance-Aware Graph Prompt Learning—0
Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models—0
Federated Domain Generalization via Prompt Learning and AggregationCode0
Prompt-enhanced Network for Hateful Meme ClassificationCode0
Best Practices for Distilling Large Language Models into BERT for Web Search Ranking—0
In the Era of Prompt Learning with Vision-Language Models—0
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification—0
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP—0
MoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval—0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking—0
Multi-Class Textual-Inversion Secretly Yields a Semantic-Agnostic ClassifierCode0
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting—0
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery—0
Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code ReviewCode0
Prompt Diffusion Robustifies Any-Modality Prompt Learning—0
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation—0
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks—0
Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation—0
On the Utility of Domain Modeling Assistance with Large Language Models—0
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts—0
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