Papers Visual Prompt Tuning
“Visual Prompt Tuning” 태그가 달린 논문 88편 · 필터 해제
DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers
Visual Prompt Tuning (VPT) has become a promising solution for Parameter-Efficient Fine-Tuning (PEFT) approach for Vision Transformer (ViT) models by partially fine-tuning learnable tokens while keeping most model parame…
Metric Learningparameter-efficient fine-tuningVisual Prompt TuningQ-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning
The rapid advancement of Large Multi-modal Foundation Models (LMM) has paved the way for the possible Explainable Image Quality Assessment (EIQA) with instruction tuning from two perspectives: overall quality explanation…
AttributeImage Quality AssessmentTransfer LearningVisual Prompt TuningVisual Variational Autoencoder Prompt Tuning
Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of full fine-tuning. While existing visual p…
Diversityparameter-efficient fine-tuningVisual Prompt TuningIterative Prompt Relocation for Distribution-Adaptive Visual Prompt Tuning
Visual prompt tuning (VPT) provides an efficient and effective solution for adapting pre-trained models to various downstream tasks by incorporating learnable prompts. However, most prior art indiscriminately applies a f…
Visual Prompt TuningExploring Interpretability for Visual Prompt Tuning with Hierarchical Concepts
Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the interpretability of this approach, which…
Visual Prompt TuningAdaptive Prompt: Unlocking the Power of Visual Prompt Tuning
Visual Prompt Tuning (VPT) has recently emerged as a powerful method for adapting pre-trained vision models to downstream tasks. By introducing learnable prompt tokens as task-specific instructions, VPT effectively guide…
Mixture-of-ExpertsVisual Prompt TuningPrompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
We present a simple usage of pre-trained Vision Transformers (ViTs) for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as different bird species or do…
Explainable Artificial Intelligence (XAI)Explainable ModelsFine-Grained Image ClassificationInterpretable Machine Learning+1Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection
Out-of-distribution (OOD) detection has seen significant advancements with zero-shot approaches by leveraging the powerful Vision-Language Models (VLMs) such as CLIP. However, prior research works have predominantly focu…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionVisual Prompt TuningMedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention
With the popularity of foundational models, parameter efficient fine tuning has become the defacto approach to leverage pretrained models to perform downstream tasks. Taking inspiration from recent advances in large lang…
ClassificationFine-Grained Image Classificationimage-classificationImage Classification+4Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies have overemphasized co-occurrence rel…
image-classificationImage ClassificationMixture-of-ExpertsMulti-Label Image Classification+1AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments
Incorporating transformer models into edge devices poses a significant challenge due to the computational demands of adapting these large models across diverse applications. Parameter-efficient tuning (PET) methods (…
Visual Prompt TuningPrompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bi…
Visual Prompt TuningDefending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning
Multimodal contrastive learning models (e.g., CLIP) can learn high-quality representations from large-scale image-text datasets, yet they exhibit significant vulnerabilities to backdoor attacks, raising serious safety co…
backdoor defenseContrastive LearningVisual Prompt TuningSemantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning
As the scale of vision models continues to grow, Visual Prompt Tuning (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance compared to full fine-tuning. However, indi…
Attributeparameter-efficient fine-tuningTransfer LearningVisual Prompt TuningAdaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning
Few-shot, fine-grained classification in computer vision poses significant challenges due to the need to differentiate subtle class distinctions with limited data. This paper presents a novel method that enhances the Con…
Few-Shot LearningVisual Prompt TuningMed-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain
Leveraging pre-trained models with tailored prompts for in-context learning has proven highly effective in NLP tasks. Building on this success, recent studies have applied a similar approach to the Segment Anything Model…
In-Context LearningPrompt EngineeringVisual Prompt TuningVisual Fourier Prompt Tuning
With the scale of vision Transformer-based models continuing to grow, finetuning these large-scale pretrained models for new tasks has become increasingly parameter-intensive. Visual prompt tuning is introduced as a para…
Visual Prompt TuningImproving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Long-tail learning has garnered widespread attention and achieved significant progress in recent times. However, even with pre-trained prior knowledge, models still exhibit weaker generalization performance on tail class…
Long-tail LearningVisual Prompt TuningTuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models
Vision-Language Models (VLMs) have shown impressive performance in vision tasks, but adapting them to new domains often requires expensive fine-tuning. Prompt tuning techniques, including textual, visual, and multimodal …
BenchmarkingSegmentationVision-Language SegmentationVisual Prompt TuningEnd-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images
Survival analysis using pathology images poses a considerable challenge, as it requires the localization of relevant information from the multitude of tiles within whole slide images (WSIs). Current methods typically res…
DecoderSurvival AnalysisSurvival PredictionVisual Prompt Tuning+1