Cover Image

Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gao, Zhengqing, Ao, Xiang, Zhang, Xu-Yao, Liu, Cheng-Lin
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Unified Entropy Optimization for Open-Set Test-Time Adaptation
by: Gao, Zhengqing, et al.
Published: (2024)

Efficient Test-Time Prompt Tuning for Vision-Language Models
by: Zhu, Yuhan, et al.
Published: (2024)

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
by: Wang, Xin, et al.
Published: (2026)

Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval
by: Wang, Yifan, et al.
Published: (2025)

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models
by: Wang, Xin, et al.
Published: (2024)

Anomaly Detection by Adapting a pre-trained Vision Language Model
by: Cai, Yuxuan, et al.
Published: (2024)

Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining
by: Jang, Hyeonseo, et al.
Published: (2026)

Modeling Variants of Prompts for Vision-Language Models
by: Li, Ao, et al.
Published: (2025)

Generalizable Prompt Tuning for Vision-Language Models
by: Zhang, Qian
Published: (2024)

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations
by: Zeng, Chengxi, et al.
Published: (2025)

Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation
by: Gao, Junyu, et al.
Published: (2023)

A-TPT: Angular Diversity Calibration Properties for Test-Time Prompt Tuning of Vision-Language Models
by: Ahamed, Shihab Aaqil, et al.
Published: (2025)

Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models
by: Hai, Jia-Wei, et al.
Published: (2026)

MetaTPT: Meta Test-time Prompt Tuning for Vision-Language Models
by: Lei, Yuqing, et al.
Published: (2025)

A Closer Look at Conditional Prompt Tuning for Vision-Language Models
by: Zhang, Ji, et al.
Published: (2025)

Prompt Tuning with Soft Context Sharing for Vision-Language Models
by: Ding, Kun, et al.
Published: (2022)

Robust Prompt Tuning for Vision-Language Models with Mild Semantic Noise
by: Gao, Yansheng, et al.
Published: (2025)

Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models
by: Song, Fei, et al.
Published: (2025)

TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
by: Yao, Hantao, et al.
Published: (2023)

Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation
by: Chng, Yong Xien, et al.
Published: (2024)

SEP: Self-Enhanced Prompt Tuning for Visual-Language Model
by: Yao, Hantao, et al.
Published: (2024)

ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt
by: Zeng, Fanhu, et al.
Published: (2024)

From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning
by: Bai, Yang, et al.
Published: (2024)

MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation
by: Huang, Jiaxin, et al.
Published: (2025)

Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models
by: Guo, Boyang, et al.
Published: (2026)

MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
by: Zhao, Shiji, et al.
Published: (2025)

What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters
by: Liu, Shaobo, et al.
Published: (2026)

O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models
by: Sharifdeen, Ashshak, et al.
Published: (2025)

Towards Calibrating Prompt Tuning of Vision-Language Models
by: Sharifdeen, Ashshak, et al.
Published: (2026)

From Tokens to Photons: Test-Time Physical Prompting for Vision-Language Models
by: Im, Boyeong, et al.
Published: (2025)

D-TPT: Dimensional Entropy Maximization for Calibrating Test-Time Prompt Tuning in Vision-Language Models
by: Han, Jisu, et al.
Published: (2025)

Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
by: Ding, Kun, et al.
Published: (2024)

Class-Aware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models
by: Qiao, Xiaozhen, et al.
Published: (2025)

StylePrompter: Enhancing Domain Generalization with Test-Time Style Priors
by: Zhang, Jiao, et al.
Published: (2024)

ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models
by: Tian, Xinyu, et al.
Published: (2023)

Tuning Vision-Language Models with Candidate Labels by Prompt Alignment
by: Zhang, Zhifang, et al.
Published: (2024)

Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
by: Liu, Xinyang, et al.
Published: (2023)

Adversarial Prompt Tuning for Vision-Language Models
by: Zhang, Jiaming, et al.
Published: (2023)

AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
by: Chang, You-Ming, et al.
Published: (2023)

Historical Test-time Prompt Tuning for Vision Foundation Models
by: Zhang, Jingyi, et al.
Published: (2024)