Cover Image

Towards Compatible Fine-tuning for Vision-Language Model Updates

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Zhengbo, Liang, Jian, Sheng, Lijun, He, Ran, Wang, Zilei, Tan, Tieniu
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models
by: Wang, Zhengbo, et al.
Published: (2024)

Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
by: Liang, Jian, et al.
Published: (2023)

The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models
by: Sheng, Lijun, et al.
Published: (2025)

A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
by: Wang, Zhengbo, et al.
Published: (2024)

R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
by: Sheng, Lijun, et al.
Published: (2025)

Exploring Vacant Classes in Label-Skewed Federated Learning
by: Guo, Kuangpu, et al.
Published: (2024)

Cooperative Pseudo Labeling for Unsupervised Federated Classification
by: Guo, Kuangpu, et al.
Published: (2025)

A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
by: Liang, Jian, et al.
Published: (2023)

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging
by: Guo, Kuangpu, et al.
Published: (2025)

Adapting Vision-Language Models Without Labels: A Comprehensive Survey
by: Dong, Hao, et al.
Published: (2025)

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
by: Wang, Zhengbo, et al.
Published: (2024)

Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
by: Wang, Zhengbo, et al.
Published: (2026)

STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
by: Yu, Yongcan, et al.
Published: (2024)

Prototypical Distillation and Debiased Tuning for Black-box Unsupervised Domain Adaptation
by: Liang, Jian, et al.
Published: (2024)

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
by: Wu, Junfei, et al.
Published: (2024)

Fine-tuned Vision Language Model for Localization of Parasitic Eggs in Microscopic Images
by: Sien, Chan Hao, et al.
Published: (2026)

Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
by: Wang, Yanbo, et al.
Published: (2024)

Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories
by: Naharas, Nilay, et al.
Published: (2025)

Fine-tuning Vision Classifiers On A Budget
by: Kumar, Sunil, et al.
Published: (2024)

Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
by: Zhou, Yiyang, et al.
Published: (2024)

Exploiting Layer Normalization Fine-tuning in Visual Transformer Foundation Models for Classification
by: Tan, Zhaorui, et al.
Published: (2025)

Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models
by: Han, Xu, et al.
Published: (2024)

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition
by: Wang, Yijun, et al.
Published: (2025)

Differentially Private Bias-Term Fine-tuning of Foundation Models
by: Bu, Zhiqi, et al.
Published: (2022)

Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models
by: Zhang, Mingyuan, et al.
Published: (2025)

Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
by: Yu, Yongcan, et al.
Published: (2025)

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models
by: Zheng, Weiying, et al.
Published: (2025)

Fairness-Aware Fine-Tuning of Vision-Language Models for Medical Glaucoma Diagnosis
by: Gu, Zijian, et al.
Published: (2025)

Personalized Preference Fine-tuning of Diffusion Models
by: Dang, Meihua, et al.
Published: (2025)

Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
by: Li, Kaican, et al.
Published: (2024)

FRISM: Fine-Grained Reasoning Injection via Subspace-Level Model Merging for Vision-Language Models
by: Huang, Chenyu, et al.
Published: (2026)

TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration
by: Li, Chunxiao, et al.
Published: (2026)

Towards Understanding How Knowledge Evolves in Large Vision-Language Models
by: Wang, Sudong, et al.
Published: (2025)

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting
by: Huang, Chen, et al.
Published: (2025)

Fine-tuning MLLMs Without Forgetting Is Easier Than You Think
by: Li, He, et al.
Published: (2026)

Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
by: Wang, Wenxiao, et al.
Published: (2024)

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks
by: Ding, Yuhe, et al.
Published: (2024)

Fine-tuning with Very Large Dropout
by: Zhang, Jianyu, et al.
Published: (2024)

ReMem: Mutual Information-Aware Fine-tuning of Pretrained Vision Transformers for Effective Knowledge Distillation
by: Dong, Chengyu, et al.
Published: (2025)

Vision Transformer with Super Token Sampling
by: Huang, Huaibo, et al.
Published: (2022)