Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification
Cloth-changing person re-identification (CC-ReID) aims to match individuals across surveillance cameras despite variations in clothing. Existing methods typically mitigate the impact of clothing changes or enhance identi…
Cloth-Changing Person Re-IdentificationPerson Re-IdentificationPrompt LearningNLPrompt: Noise-Label Prompt Learning for Vision-Language Models
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often c…
Learning TheoryLearning with noisy labelsPrompt LearningForensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection
We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is non-tri…
Prompt LearningQUOTA: Quantifying Objects with Text-to-Image Models for Any Domain
We tackle the problem of quantifying the number of objects by a generative text-to-image model. Rather than retraining such a model for each new image domain of interest, which leads to high computational costs and limit…
Domain GeneralizationImage GenerationMeta-LearningObject+3Instance-Aware Graph Prompt Learning
Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availability of high-quality labels in an end-t…
Graph Representation LearningPrompt LearningRepresentation LearningStyle-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models
Pre-trained Vision-language (VL) models, such as CLIP, have shown significant generalization ability to downstream tasks, even with minimal fine-tuning. While prompt learning has emerged as an effective strategy to adapt…
Domain GeneralizationPrompt LearningZero-shot GeneralizationPromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation
Recent advances in All-in-One (AiO) RGB image restoration have demonstrated the effectiveness of prompt learning in handling multiple degradations within a single model. However, extending these approaches to hyperspectr…
Image RestorationLanguage ModelingLanguage ModellingPrompt LearningBiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effectively adapting VLMs to downstream appl…
image-classificationImage ClassificationKnowledge DistillationPrompt Engineering+2UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning
The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential for understanding and optimizing urban s…
ImputationMulti-Task LearningPrompt LearningContourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution
Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image …
Image EnhancementImage Super-ResolutionInfrared image super-resolutionPrompt Learning+1Federated Domain Generalization via Prompt Learning and Aggregation
Federated domain generalization (FedDG) aims to improve the global model generalization in unseen domains by addressing data heterogeneity under privacy-preserving constraints. A common strategy in existing FedDG studies…
Domain GeneralizationPrivacy PreservingPrompt LearningTransfer LearningGolden Noise for Diffusion Models: A Learning Framework
Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some noises are ``golden noises'' that can achiev…
Prompt LearningPrompt-enhanced Network for Hateful Meme Classification
The dynamic expansion of social media has led to an inundation of hateful memes on media platforms, accentuating the growing need for efficient identification and removal. Acknowledging the constraints of conventional mu…
ClassificationContrastive LearningHateful Meme ClassificationLanguage Modelling+2GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection
Zero-shot anomaly detection (ZSAD) is crucial for detecting anomalous patterns in target datasets without using training samples, specifically in scenarios where there are distributional differences between the target do…
Anomaly DetectionContrastive LearningPrompt Learningzero-shot anomaly detectionIn the Era of Prompt Learning with Vision-Language Models
Large-scale foundation models like CLIP have shown strong zero-shot generalization but struggle with domain shifts, limiting their adaptability. In our work, we introduce \textsc{StyLIP}, a novel domain-agnostic prompt l…
Domain AdaptationDomain GeneralizationPrompt LearningSemantic Segmentation+2Best Practices for Distilling Large Language Models into BERT for Web Search Ranking
Recent studies have highlighted the significant potential of Large Language Models (LLMs) as zero-shot relevance rankers. These methods predominantly utilize prompt learning to assess the relevance between queries and do…
Prompt LearningAgainst Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective yet efficient federated optimization ag…
Graph LearningPrompt LearningTransfer LearningFPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learn…
Continual LearningContrastive LearningFederated LearningPrompt LearningNot Just Object, But State: Compositional Incremental Learning without Forgetting
Most incremental learners excessively prioritize coarse classes of objects while neglecting various kinds of states (e.g. color and material) attached to the objects. As a result, they are limited in the ability to reaso…
DiversityIncremental LearningObjectPrompt LearningInvestigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical comple…
Binary ClassificationComplex Word IdentificationLexical Complexity PredictionLexical Simplification+2