Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
Multiple Consistency-guided Test-Time Adaptation for Contrastive Audio-Language Models with Unlabeled Audio
One fascinating aspect of pre-trained Audio-Language Models (ALMs) learning is their impressive zero-shot generalization capability and test-time adaptation (TTA) methods aiming to improve domain performance without anno…
Contrastive LearningPrompt LearningTest-time Adaptationzero-shot-classification+2Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation
Recent efforts on image restoration have focused on developing "all-in-one" models that can handle different degradation types and levels within single model. However, most of mainstream Transformer-based ones confronted…
DenoisingImage DenoisingImage RestorationMamba+2Prompt-based Unifying Inference Attack on Graph Neural Networks
Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning capabilities on graph data. Nevertheless, …
DisentanglementInference AttackPrompt LearningPLPP: Prompt Learning with Perplexity Is Self-Distillation for Vision-Language Models
Pre-trained Vision-Language (VL) models such as CLIP have demonstrated their excellent performance across numerous downstream tasks. A recent method, Context Optimization (CoOp), further improves the performance of VL mo…
Prompt LearningPromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts
Multi-camera 3D object detection aims to detect and localize objects in 3D space using multiple cameras, which has attracted more attention due to its cost-effectiveness trade-off. However, these methods often struggle w…
3D Object DetectionDepth EstimationKnowledge DistillationObject+3Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion
Virtual staining leverages computer-aided techniques to transfer the style of histochemically stained tissue samples to other staining types. In virtual staining of pathological images, maintaining strict structural cons…
DiagnosticDisentanglementPrompt LearningStyle Transfer+1CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation
Recent advances in agentic LLMs have demonstrated great capabilities in Verilog code generation. However, existing approaches either use LLM-assisted single-agent prompting or cooperation-only multi-agent learning, which…
Code GenerationPrompt LearningPEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning
Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions. Researchers are curr…
class-incremental learningClass Incremental LearningIncremental LearningPrompt LearningPrompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks 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 can m…
Prompt LearningATPrompt: Textual Prompt Learning with Embedded Attributes
Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text prompt inputs, aiming to align image and text (category) spaces for downstream ta…
AttributeLarge Language ModelPrompt LearningCAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning
Recently, prompt tuning methods for pre-trained models have demonstrated promising performance in Class Incremental Learning (CIL). These methods typically involve learning task-specific prompts and predicting the task I…
class-incremental learningClass Incremental LearningIncremental LearningPrompt LearningBayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation
The emergence of large pre-trained vision-language models (VLMs) represents a paradigm shift in machine learning, with unprecedented results in a broad span of visual recognition tasks. CLIP, one of the most popular VLMs…
Bayesian InferencePrompt LearningTransfer LearningCan Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over internet-scale image-text pairs, building transferable Graph Neural Networks (GNNs) with CLIP pipel…
Prompt Learningzero-shot-classificationZero-Shot LearningTextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning
Despite the efficiency of prompt learning in transferring vision-language models (VLMs) to downstream tasks, existing methods mainly learn the prompts in a coarse-grained manner where the learned prompt vectors are share…
Prompt LearningLeveraging Content and Context Cues for Low-Light Image Enhancement
Low-light conditions have an adverse impact on machine cognition, limiting the performance of computer vision systems in real life. Since low-light data is limited and difficult to annotate, we focus on image processing …
Data AugmentationFace Detectionimage-classificationImage Classification+3Attention Head Purification: A New Perspective to Harness CLIP for Domain Generalization
Domain Generalization (DG) aims to learn a model from multiple source domains to achieve satisfactory performance on unseen target domains. Recent works introduce CLIP to DG tasks due to its superior image-text alignment…
Domain GeneralizationPrompt LearningLeveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection
Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer's disease (AD) detection methods are characterized by their non-invasiveness, cost-effectiveness, and convenience. Previous s…
Alzheimer's Disease DetectionAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Language Modeling+5Are foundation models for computer vision good conformal predictors?
Recent advances in self-supervision and constrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progress, these models are becoming the prevai…
Conformal PredictionPrompt LearningMemory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Temporal Graph Neural Networks (STGNNs) hav…
Prompt LearningPersonalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis
The task of image captioning demands an algorithm to generate natural language descriptions of visual inputs. Recent advancements have seen a convergence between image captioning research and the development of Large Lan…
Image CaptioningImage DescriptionPrompt Learning