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Prompt Learning
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
MoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval
EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking
Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Multi-Class Textual-Inversion Secretly Yields a Semantic-Agnostic Classifier
Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis
Prompt Diffusion Robustifies Any-Modality Prompt Learning
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation
IPO: Interpretable Prompt Optimization for Vision-Language Models
A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts
Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation
On the Utility of Domain Modeling Assistance with Large Language Models
Tree of Attributes Prompt Learning for Vision-Language Models
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
LOBG:Less Overfitting for Better Generalization in Vision-Language Model
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt
Animating the Past: Reconstruct Trilobite via Video Generation
Deep Correlated Prompting for Visual Recognition with Missing Modalities
ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models
In-Context Code-Text Learning for Bimodal Software Engineering
Item Cluster-aware Prompt Learning for Session-based Recommendation
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