Visual Prompt Tuning
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Benchmarks
Most implemented
Visual Prompt Tuning
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
DINO-VPT: Hierarchical Visual Prompt Tuning for Joint Physical-Digital Face Anti-Spoofing
Attention to Burstiness: Low-Rank Bilinear Prompt Tuning
Papers
When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning
Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot setups. However, their performance depends on the quality of prompts an…
Medical Image SegmentationVisual Prompt TuningDINO-VPT: Hierarchical Visual Prompt Tuning for Joint Physical-Digital Face Anti-Spoofing
With the increasing diversity of spoofing attacks, there is a growing demand for unified Face Anti-Spoofing (FAS) models capable of detecting both physical and digital threats. While existing Vision-Language Models (VLMs…
Visual Prompt TuningFace Anti-SpoofingFedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates…
Knowledge DistillationVisual Prompt TuningFederated LearningHow many labels do you need? A decision framework for cross-habitat marine species recognition
Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on how much labelling effort reliable deployment at new sites requires…
Visual Prompt TuningLayer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models
Visual prompt tuning has emerged as a parameter-efficient fine-tuning approach for adapting large-scale Vision Transformers (ViTs) to downstream tasks. As its learnable prompts are applied in input and feature spaces, pr…
parameter-efficient fine-tuningVisual Prompt TuningSparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery
Generalized Category Discovery (GCD) seeks to identify novel categories from unlabeled data while retaining the classification ability of seen categories. Prior GCD methods commonly leverage transferable representations …
Visual Prompt Tuning