Papers Visual Prompt Tuning
“Visual Prompt Tuning” 태그가 달린 논문 88편 · 필터 해제
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task
In recent years, the rapid expansion of model sizes has led to large-scale pre-trained models demonstrating remarkable capabilities. Consequently, there has been a trend towards increasing the scale of models. However, t…
parameter-efficient fine-tuningVisual Prompt TuningSAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning
Advances in Parameter-Efficient Fine-Tuning (PEFT) bridged the performance gap with Full Fine-Tuning (FFT) through sophisticated analysis of pre-trained parameter spaces. Starting from drawing insights from Neural Engram…
parameter-efficient fine-tuningVisual Prompt TuningProbing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification
With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of data silos, and stringent privacy constrain…
Federated Learningimage-classificationImage ClassificationMedical Image Classification+2Visual Prompt Tuning in Null Space for Continual Learning
Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL), by selecting and updating relevant prompts in the vision-transformer models. On the contrary, this paper aims to learn …
Continual LearningVisual Prompt TuningTAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt
The recent introduction of prompt tuning based on pre-trained vision-language models has dramatically improved the performance of multi-label image classification. However, some existing strategies that have been explore…
Diversityimage-classificationImage ClassificationMulti-Label Image Classification+1iVPT: Improving Task-relevant Information Sharing in Visual Prompt Tuning by Cross-layer Dynamic Connection
Recent progress has shown great potential of visual prompt tuning (VPT) when adapting pre-trained vision transformers to various downstream tasks. However, most existing solutions independently optimize prompts at each l…
image-classificationImage ClassificationSemantic SegmentationVisual Prompt TuningECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning
Panoptic segmentation, combining semantic and instance segmentation, stands as a cutting-edge computer vision task. Despite recent progress with deep learning models, the dynamic nature of real-world applications necessi…
Continual LearningContinual Panoptic SegmentationInstance SegmentationKnowledge Distillation+4CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source domains to unlabeled target domains. When adapting to adverse scenes, existing UDA methods fail to perform well due to the lack of instruction…
Domain AdaptationSemantic SegmentationUnsupervised Domain AdaptationVisual Prompt TuningLSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning
Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnable tokens termed as prompts. Contemporar…
Representation LearningVisual Prompt TuningCoLLaVO: Crayon Large Language and Vision mOdel
The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet, it remains unexplored whether current VL…
Large Language ModelmodelObjectVisual Prompt Tuning+3Revisiting the Power of Prompt for Visual Tuning
Visual prompt tuning (VPT) is a promising solution incorporating learnable prompt tokens to customize pre-trained models for downstream tasks. However, VPT and its variants often encounter challenges like prompt initiali…
Visual Prompt TuningFacing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to tradition…
Transfer LearningVisual Prompt TuningPlug-and-Play Transformer Modules for Test-Time Adaptation
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model. However, the num…
Domain AdaptationTest-time AdaptationVisual Prompt TuningDisentangled Prompt Representation for Domain Generalization
Domain Generalization (DG) aims to develop a versatile model capable of performing well on unseen target domains. Recent advancements in pre-trained Visual Foundation Models (VFMs) such as CLIP show significant poten…
Domain GeneralizationLanguage ModellingLarge Language ModelVisual Prompt TuningFair-VPT: Fair Visual Prompt Tuning for Image Classification
Despite the remarkable success of Vision Transformers (ViT) across diverse fields in computer vision they have a clear drawback of expensive adaption cost for downstream tasks due to the increased scale. To address t…
Contrastive LearningDisentanglementFairnessimage-classification+2Open Vocabulary Semantic Scene Sketch Understanding
We study the underexplored but fundamental vision problem of machine understanding of abstract freehand scene sketches. We introduce a sketch encoder that results in semantically-aware feature space, which we evaluate by…
DisentanglementVisual Prompt TuningSA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt
As a prominent parameter-efficient fine-tuning technique in NLP, prompt tuning is being explored its potential in computer vision. Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming…
image-classificationImage Classificationparameter-efficient fine-tuningVisual Prompt TuningProgressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression
In this paper, we propose a progressive learning paradigm for transformer-based variable-rate image compression. Our approach covers a wide range of compression rates with the assistance of the Layer-adaptive Prompt Modu…
DecoderImage CompressionVisual Prompt TuningTowards Few-shot Out-of-Distribution Detection
Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems. Despite the notable advancements in existing OOD detection methodologies, our study identifies a significant…
General KnowledgeOut-of-Distribution DetectionOut of Distribution (OOD) DetectionVisual Prompt TuningTSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding
Holistic scene understanding includes semantic segmentation, surface normal estimation, object boundary detection, depth estimation, etc. The key aspect of this problem is to learn representation effectively, as each sub…
Boundary DetectionDepth EstimationRepresentation LearningScene Understanding+3