Saved in:
| Main Authors: | Tsao, Hsi-Ai, Hsiung, Lei, Chen, Pin-Yu, Ho, Tsung-Yi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.01821 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AutoVP: An Automated Visual Prompting Framework and Benchmark
by: Tsao, Hsi-Ai, et al.
Published: (2023)
by: Tsao, Hsi-Ai, et al.
Published: (2023)
RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
by: He, Zhiyuan, et al.
Published: (2024)
by: He, Zhiyuan, et al.
Published: (2024)
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
by: Sun, Hao-Lun, et al.
Published: (2023)
by: Sun, Hao-Lun, et al.
Published: (2023)
Modular Prompt Learning Improves Vision-Language Models
by: Huang, Zhenhan, et al.
Published: (2025)
by: Huang, Zhenhan, et al.
Published: (2025)
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
by: Chou, Sheng-Yen, et al.
Published: (2023)
by: Chou, Sheng-Yen, et al.
Published: (2023)
Differentiable Prompt Learning for Vision Language Models
by: Huang, Zhenhan, et al.
Published: (2024)
by: Huang, Zhenhan, et al.
Published: (2024)
Visual Prompt Engineering for Vision Language Models in Radiology
by: Denner, Stefan, et al.
Published: (2024)
by: Denner, Stefan, et al.
Published: (2024)
Concept-Guided Prompt Learning for Generalization in Vision-Language Models
by: Zhang, Yi, et al.
Published: (2024)
by: Zhang, Yi, et al.
Published: (2024)
When Visual Evidence is Ambiguous: Pareidolia as a Diagnostic Probe for Vision Models
by: Chen, Qianpu, et al.
Published: (2026)
by: Chen, Qianpu, et al.
Published: (2026)
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model
by: Ai, Zixiang, et al.
Published: (2025)
by: Ai, Zixiang, et al.
Published: (2025)
Structural Graph Probing of Vision-Language Models
by: He, Haoyu, et al.
Published: (2026)
by: He, Haoyu, et al.
Published: (2026)
Mixture of Prompt Learning for Vision Language Models
by: Du, Yu, et al.
Published: (2024)
by: Du, Yu, et al.
Published: (2024)
When Does Pruning Benefit Vision Representations?
by: Cassano, Enrico, et al.
Published: (2025)
by: Cassano, Enrico, et al.
Published: (2025)
Reasoning or Pattern Matching? Probing Large Vision-Language Models with Visual Puzzles
by: Lymperaiou, Maria, et al.
Published: (2026)
by: Lymperaiou, Maria, et al.
Published: (2026)
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations
by: Zhu, Kangyu, et al.
Published: (2025)
by: Zhu, Kangyu, et al.
Published: (2025)
FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
by: Singha, Mainak, et al.
Published: (2025)
by: Singha, Mainak, et al.
Published: (2025)
VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference
by: Zhu, Hao, et al.
Published: (2026)
by: Zhu, Hao, et al.
Published: (2026)
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
by: Chang, You-Ming, et al.
Published: (2023)
by: Chang, You-Ming, et al.
Published: (2023)
When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models
by: Hou, Jiacheng, et al.
Published: (2026)
by: Hou, Jiacheng, et al.
Published: (2026)
VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models
by: Reilly, Dominick, et al.
Published: (2025)
by: Reilly, Dominick, et al.
Published: (2025)
VPTracker: Global Vision-Language Tracking via Visual Prompt
by: Wang, Jingchao, et al.
Published: (2025)
by: Wang, Jingchao, et al.
Published: (2025)
When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations
by: Lalai, Harsh Nishant, et al.
Published: (2026)
by: Lalai, Harsh Nishant, et al.
Published: (2026)
Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving
by: Theodoridis, Nikos, et al.
Published: (2026)
by: Theodoridis, Nikos, et al.
Published: (2026)
Attention Prompting on Image for Large Vision-Language Models
by: Yu, Runpeng, et al.
Published: (2024)
by: Yu, Runpeng, et al.
Published: (2024)
Vision Graph Prompting via Semantic Low-Rank Decomposition
by: Ai, Zixiang, et al.
Published: (2025)
by: Ai, Zixiang, et al.
Published: (2025)
Modeling Variants of Prompts for Vision-Language Models
by: Li, Ao, et al.
Published: (2025)
by: Li, Ao, et al.
Published: (2025)
MetaTPT: Meta Test-time Prompt Tuning for Vision-Language Models
by: Lei, Yuqing, et al.
Published: (2025)
by: Lei, Yuqing, et al.
Published: (2025)
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
by: Li, Zheng, et al.
Published: (2024)
by: Li, Zheng, et al.
Published: (2024)
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
by: Chen, Yu-Jen, et al.
Published: (2025)
by: Chen, Yu-Jen, et al.
Published: (2025)
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
by: Hsu, Chia-Yi, et al.
Published: (2025)
by: Hsu, Chia-Yi, et al.
Published: (2025)
SEP: Self-Enhanced Prompt Tuning for Visual-Language Model
by: Yao, Hantao, et al.
Published: (2024)
by: Yao, Hantao, et al.
Published: (2024)
Contextualized Visual Personalization in Vision-Language Models
by: Oh, Yeongtak, et al.
Published: (2026)
by: Oh, Yeongtak, et al.
Published: (2026)
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
by: Chin, Zhi-Yi, et al.
Published: (2023)
by: Chin, Zhi-Yi, et al.
Published: (2023)
Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention
by: Song, Shezheng, et al.
Published: (2026)
by: Song, Shezheng, et al.
Published: (2026)
Adversarial Prompt Distillation for Vision-Language Models
by: Luo, Lin, et al.
Published: (2024)
by: Luo, Lin, et al.
Published: (2024)
Active Prompt Learning in Vision Language Models
by: Bang, Jihwan, et al.
Published: (2023)
by: Bang, Jihwan, et al.
Published: (2023)
In the Era of Prompt Learning with Vision-Language Models
by: Jha, Ankit
Published: (2024)
by: Jha, Ankit
Published: (2024)
Revisiting Prompt Pretraining of Vision-Language Models
by: Chen, Zhenyuan, et al.
Published: (2024)
by: Chen, Zhenyuan, et al.
Published: (2024)
Generalizable Prompt Tuning for Vision-Language Models
by: Zhang, Qian
Published: (2024)
by: Zhang, Qian
Published: (2024)
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
by: Lee, Heekyung, et al.
Published: (2025)
by: Lee, Heekyung, et al.
Published: (2025)
Similar Items
-
AutoVP: An Automated Visual Prompting Framework and Benchmark
by: Tsao, Hsi-Ai, et al.
Published: (2023) -
RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
by: He, Zhiyuan, et al.
Published: (2024) -
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
by: Sun, Hao-Lun, et al.
Published: (2023) -
Modular Prompt Learning Improves Vision-Language Models
by: Huang, Zhenhan, et al.
Published: (2025) -
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
by: Chou, Sheng-Yen, et al.
Published: (2023)