Papers Visual Prompt Tuning
“Visual Prompt Tuning” 태그가 달린 논문 88편 · 필터 해제
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. This suggests a promising paradigm shift of adapting pre-trained Vi…
Federated LearningPersonalized Federated LearningVisual Prompt TuningTask-driven Prompt Evolution for Foundation Models
Promptable foundation models, particularly Segment Anything Model (SAM), have emerged as a promising alternative to the traditional task-specific supervised learning for image segmentation. However, many evaluation studi…
Image SegmentationSegmentationSemantic SegmentationVisual Prompt TuningFrom Question to Exploration: Test-Time Adaptation in Semantic Segmentation?
Test-time adaptation (TTA) aims to adapt a model, initially trained on training data, to test data with potential distribution shifts. Most existing TTA methods focus on classification problems. The pronounced success of…
ClassificationSegmentationSemantic SegmentationTest-time Adaptation+1VPA: Fully Test-Time Visual Prompt Adaptation
Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-engineered prompts as trainable parameters. Ins…
Domain AdaptationPseudo LabelTest-time AdaptationVisual Prompting+2MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
Vision Transformer (ViT) models have recently emerged as powerful and versatile models for various visual tasks. Recently, a work called PMF has achieved promising results in few-shot image classification by utilizing pr…
Data AugmentationDiversityDomain AdaptationFew-Shot Image Classification+5Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning
Parameter efficient transfer learning (PETL) is an emerging research spot that aims to adapt large-scale pre-trained models to downstream tasks. Recent advances have achieved great success in saving storage and computati…
Transfer LearningVisual Prompt TuningLoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models
Prompt engineering is a powerful tool used to enhance the performance of pre-trained models on downstream tasks. For example, providing the prompt "Let's think step by step" improved GPT-3's reasoning accuracy to 63% on …
Domain GeneralizationFew-Shot LearningPrompt EngineeringPrompt Learning+1Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning
Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world scenarios, the number of input data and…
class-incremental learningClass Incremental LearningContinual LearningIncremental Learning+1SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation
Medical image analysis using deep learning is often challenged by limited labeled data and high annotation costs. Fine-tuning the entire network in label-limited scenarios can lead to overfitting and suboptimal performan…
Lesion SegmentationMedical Image AnalysisTumor SegmentationVisual Prompt TuningE^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
As the size of transformer-based models continues to grow, fine-tuning these large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. Parameter-efficient learning has been developed…
Visual Prompt TuningDVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis
Limited labeled data makes it hard to train models from scratch in medical domain, and an important paradigm is pre-training and then fine-tuning. Large pre-trained models contain rich representations, which can be adapt…
Medical Image Analysisparameter-efficient fine-tuningVisual Prompt TuningUncertainty-aware State Space Transformer for Egocentric 3D Hand Trajectory Forecasting
Hand trajectory forecasting from egocentric views is crucial for enabling a prompt understanding of human intentions when interacting with AR/VR systems. However, existing methods handle this problem in a 2D image space …
3D Human Pose TrackingTrajectory ForecastingTrajectory PredictionVisual Prompt TuningImproving Visual Prompt Tuning for Self-supervised Vision Transformers
Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks. It leverages extra learnable tokens, known as prompts, which steer the frozen pretrained Vi…
image-classificationImage ClassificationSemantic SegmentationVisual Prompt TuningTransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception
This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framewo…
DecoderImage CompressionVisual Prompt TuningLearning Disentangled Prompts for Compositional Image Synthesis
We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to better understand the compositional imag…
Domain AdaptationImage GenerationVisual Prompt TuningDo We Really Need a Large Number of Visual Prompts?
Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored. Among various methods, Visual Prompt Tuning (VPT), prepending learnable prompts…
Transfer LearningVisual Prompt TuningPVP: Pre-trained Visual Parameter-Efficient Tuning
Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high c…
Fine-Grained Image ClassificationImage ClassificationVisual Prompt TuningInstance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of full fine-tuning in downstream tasks leads …
3D Parameter-Efficient Fine-Tuning for Classification3D Point Cloud ClassificationFew-Shot 3D Point Cloud ClassificationVisual Prompt TuningGradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to conditio…
Domain GeneralizationFew-Shot LearningMeta-LearningPrompt Learning+1Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP's performance on new tasks. In thi…
Adversarial RobustnessContrastive LearningVisual Prompt Tuning