Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation
Vision-language models (VLMs) such as CLIP demonstrate strong performance but struggle when adapted to downstream tasks. Prompt learning has emerged as an efficient and effective strategy to adapt VLMs while preserving t…
Domain GeneralizationLanguage ModelingLanguage ModellingPrompt Learning+1Modeling Variants of Prompts for Vision-Language Models
Large pre-trained vision-language models (VLMs) offer a promising approach to leveraging human language for enhancing downstream tasks. However, VLMs such as CLIP face significant limitation: its performance is highly se…
Prompt EngineeringPrompt LearningSensitivityCAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
Effectively handling the co-occurrence of non-IID data and long-tailed distributions remains a critical challenge in federated learning. While fine-tuning vision-language models (VLMs) like CLIP has shown to be promising…
Federated LearningLanguage ModelingLanguage ModellingPrompt LearningXR-VLM: Cross-Relationship Modeling with Multi-part Prompts and Visual Features for Fine-Grained Recognition
Vision-Language Models (VLMs) have demonstrated impressive performance on various visual tasks, yet they still require adaptation on downstream tasks to achieve optimal performance. Recently, various adaptation technolog…
Fine-Grained Visual RecognitionPrompt LearningTowards Universal Learning-based Model for Cardiac Image Reconstruction: Summary of the CMRxRecon2024 Challenge
Cardiovascular magnetic resonance (CMR) imaging offers diverse contrasts for non-invasive assessment of cardiac function and myocardial characterization. However, CMR often requires the acquisition of many contrasts, and…
BenchmarkingImage ReconstructionOut-of-Distribution GeneralizationPrompt Learning+1Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models
Recent advancements in deep learning have shown significant potential for classifying retinal diseases using color fundus images. However, existing works predominantly rely exclusively on image data, lack interpretabilit…
DiagnosticPrompt LearningGeneralizable Prompt Learning of CLIP: A Brief Overview
Existing vision-language models (VLMs) such as CLIP have showcased an impressive capability to generalize well across various downstream tasks. These models leverage the synergy between visual and textual information, en…
Prompt LearningDiversity Covariance-Aware Prompt Learning for Vision-Language Models
Prompt tuning can further enhance the performance of visual-language models across various downstream tasks (e.g., few-shot learning), enabling them to better adapt to specific applications and needs. In this paper, we p…
DiversityFew-Shot LearningPrompt LearningInteractive Gadolinium-Free MRI Synthesis: A Transformer with Localization Prompt Learning
Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for tumor detection and diagnosis, but the use of gadolinium-based contrast agents (GBCAs) in clinical settings raises safety concerns due to potential hea…
DiagnosticPrompt LearningLLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection
As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants. These online activities raise significant soci…
Large Language ModelPrompt LearningAutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models
Popularity prediction in information cascades plays a crucial role in social computing, with broad applications in viral marketing, misinformation control, and content recommendation. However, information propagation mec…
MarketingMisinformationPredictionPrompt Learning+2Zero-Shot Semantic Communication with Multimodal Foundation Models
Most existing semantic communication (SemCom) systems use deep joint source-channel coding (DeepJSCC) to encode task-specific semantics in a goal-oriented manner. However, their reliance on predefined tasks and datasets …
Prompt LearningSemantic CommunicationA Similarity Paradigm Through Textual Regularization Without Forgetting
Prompt learning has emerged as a promising method for adapting pre-trained visual-language models (VLMs) to a range of downstream tasks. While optimizing the context can be effective for improving performance on specific…
Domain GeneralizationFew-Shot LearningPrompt LearningUnlocking Multimodal Integration in EHRs: A Prompt Learning Framework for Language and Time Series Fusion
Large language models (LLMs) have shown remarkable performance in vision-language tasks, but their application in the medical field remains underexplored, particularly for integrating structured time series data with uns…
Anomaly DetectionPrompt LearningTime SeriesModular Prompt Learning Improves Vision-Language Models
Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encoders, elicits the potentials of pre-traine…
Prompt LearningExploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection
Anomaly detection (AD) in 3D point clouds is crucial in a wide range of industrial applications, especially in various forms of precision manufacturing. Considering the industrial demand for reliable 3D AD, several metho…
3D Anomaly DetectionAnomaly DetectionPrompt LearningCLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs
Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph prompting typically use feature prompts to…
Graph ReconstructionNode ClassificationPrompt LearningGCoT: Chain-of-Thought Prompt Learning for Graphs
Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we de…
Prompt LearningMGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification
Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a prompt learning method to adapt large vis…
Contrastive LearningData Augmentationimage-classificationImage Classification+3Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image Denoising
Many studies have concentrated on constructing supervised models utilizing paired datasets for image denoising, which proves to be expensive and time-consuming. Current self-supervised and unsupervised approaches typical…
DenoisingImage DenoisingPrompt Learning