Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models
Remote sensing data is often distributed across multiple institutions, and due to privacy concerns and data-sharing restrictions, leveraging large-scale datasets in a centralized training framework is challenging. Federa…
Federated Learningimage-classificationImage ClassificationPrompt Learning+3Knowledge-Guided Prompt Learning for Deepfake Facial Image Detection
Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. …
Face SwappingPrompt LearningPrompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attack on Breast Ultrasound Images
Deep neural networks (DNNs) offer significant promise for improving breast cancer diagnosis in medical imaging. However, these models are highly susceptible to adversarial attacks--small, imperceptible changes that …
Adversarial AttackPrompt LearningLearning Conditional Space-Time Prompt Distributions for Video Class-Incremental Learning
Recent advancements in prompt-based learning have significantly advanced image and video class-incremental learning. However, the prompts learned by these methods often fail to capture the diverse and informative cha…
class-incremental learningClass Incremental LearningIncremental LearningPrompt LearningJoint Scheduling of Causal Prompts and Tasks for Multi-Task Learning
Multi-task prompt learning has emerged as a promising technique for fine-tuning pre-trained Vision-Language Models (VLMs) to various downstream tasks. However, existing methods ignore challenges caused by spurious co…
Multi-Task LearningPrompt LearningSchedulingSemantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition
Skeleton-based human action recognition is promising due to its privacy preservation, robustness to visual challenges, and computational efficiency. Especially, the practical necessity to recognize unseen actions has…
Action RecognitionComputational EfficiencyPrompt LearningSkeleton Based Action Recognition+4Query Efficient Black-Box Visual Prompting with Subspace Learning
Visual Prompt Learning (VPL) has emerged as a powerful strategy for harnessing the capabilities of large-scale pre-trained models (PTMs) to tackle specific downstream tasks. However, the opaque nature of PTMs in many…
Prompt LearningVisual PromptingA3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment
In the age of pervasive machine learning applications, protecting digital content from unauthorized use has become a pressing concern. Unlearnable examples (UEs)--data modified with imperceptible perturbations to inh…
Few-Shot LearningPrompt LearningEnhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition
Pedestrian attribute recognition (PAR) seeks to predict multiple semantic attributes associated with a specific pedestrian. There are two types of approaches for PAR: unimodal framework and bimodal framework. The for…
AttributePedestrian Attribute RecognitionPerson Re-IdentificationPrompt LearningPreserving Clusters in Prompt Learning for Unsupervised Domain Adaptation
Recent approaches leveraging multi-modal pre-trained models like CLIP for Unsupervised Domain Adaptation (UDA) have shown significant promise in bridging domain gaps and improving generalization by utilizing rich sem…
Domain AdaptationPrompt LearningUnsupervised Domain AdaptationDistilled Prompt Learning for Incomplete Multimodal Survival Prediction
The integration of multimodal data including pathology images and gene profiles is widely applied in precise survival prediction. Despite recent advances in multimodal survival models, collecting complete modalities …
PredictionPrompt LearningSurvival PredictionLearning Textual Prompts for Open-World Semi-Supervised Learning
Traditional semi-supervised learning achieves significant success in closed-world scenarios. To better align with the openness of the real world, researchers propose open-world semi-supervised learning (OWSSL), which…
Image-text matchingOpen-World Semi-Supervised LearningPrompt LearningText MatchingTask-Aware Clustering for Prompting Vision-Language Models
Prompt learning has attracted widespread attention in adapting vision-language models to downstream tasks. Existing methods largely rely on optimization strategies to ensure the task-awareness of learnable prompts. D…
ClusteringDomain GeneralizationPrompt LearningPromptHash:Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval
Cross-modal hashing is a promising approach for efficient data retrieval and storage optimization. However, contemporary methods exhibit significant limitations in semantic preservation, contextual integrity, and inf…
Contrastive LearningImage RetrievalImage to textPrompt Learning+1Differentiable Prompt Learning for Vision Language Models
Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning them into differentiable vectors. Deep con…
Prompt LearningVisual Style Prompt Learning Using Diffusion Models for Blind Face Restoration
Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior…
Blind Face RestorationPrompt LearningMoPD: Mixture-of-Prompts Distillation for Vision-Language Models
Soft prompt learning methods are effective for adapting vision-language models (VLMs) to downstream tasks. Nevertheless, empirical evidence reveals a tendency of existing methods that they overfit seen classes and exhibi…
Prompt LearningCLIP-Based Modality Compensation for Visible-Infrared Image Re-Identification
Visible-infrared image re-identification (VIReID) aims to match objects with the same identity appearing across different modalities. Given the significant differences between visible and infrared images, VIReID poses a …
Cross-Modal Person Re-IdentificationCross-Modal Person Re-IdentificationPerson Re-IdentificationPrompt LearningPrompt Tuning for Item Cold-start Recommendation
The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt learning, a powerful technique used in natur…
Prompt LearningRecommendation SystemsKernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
Few-shot anomaly detection (FSAD) aims to detect unseen anomaly regions with the guidance of very few normal support images from the same class. Existing FSAD methods usually find anomalies by directly designing complex …
Anomaly DetectionPrompt Learning