Cover Image

LPT: Less-overfitting Prompt Tuning for Vision-Language Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ding, Chenhao, Gao, Xinyuan, Dong, Songlin, Han, Jizhou, Wang, Qiang, Zhou, Zhengdong, He, Yuhang, Gong, Yihong
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Diversity Covariance-Aware Prompt Learning for Vision-Language Models
by: Dong, Songlin, et al.
Published: (2025)

Class-Independent Increment: An Efficient Approach for Multi-label Class-Incremental Learning
by: Ding, Chenhao, et al.
Published: (2025)

Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models
by: Dong, Songlin, et al.
Published: (2025)

Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental Learning
by: Han, Jizhou, et al.
Published: (2025)

Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning
by: Gao, Xinyuan, et al.
Published: (2024)

Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptation
by: Han, Jizhou, et al.
Published: (2025)

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
by: Wang, Qiang, et al.
Published: (2025)

Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery
by: Han, Jizhou, et al.
Published: (2025)

Space Rotation with Basis Transformation for Training-free Test-Time Adaptation
by: Ding, Chenhao, et al.
Published: (2025)

GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery
by: Han, Jizhou, et al.
Published: (2026)

Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery
by: Han, Jizhou, et al.
Published: (2026)

CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
by: Gao, Xinyuan, et al.
Published: (2024)

P2L-CA: An Effective Parameter Tuning Framework for Rehearsal-Free Multi-Label Class-Incremental Learning
by: Dong, Songlin, et al.
Published: (2026)

Shared & Domain Self-Adaptive Experts with Frequency-Aware Discrimination for Continual Test-Time Adaptation
by: Zhao, JianChao, et al.
Published: (2025)

VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection
by: Wang, Qiang, et al.
Published: (2025)

DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype
by: Wang, Qiang, et al.
Published: (2025)

Trajectory-Diversity-Driven Robust Vision-and-Language Navigation
by: Li, Jiangyang, et al.
Published: (2026)

Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
by: Wan, Cong, et al.
Published: (2024)

LPT++: Efficient Training on Mixture of Long-tailed Experts
by: Dong, Bowen, et al.
Published: (2024)

GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task
by: Lu, Shiwei, et al.
Published: (2026)

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

I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
by: Dong, Songlin, et al.
Published: (2024)

IOTA: Corrective Knowledge-Guided Prompt Learning via Black-White Box Framework
by: Wang, Shaokun, et al.
Published: (2026)

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

Is Parameter Isolation Better for Prompt-Based Continual Learning?
by: Li, Jiangyang, et al.
Published: (2026)

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

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

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

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

MAO: Efficient Model-Agnostic Optimization of Prompt Tuning for Vision-Language Models
by: Li, Haoyang, et al.
Published: (2025)

Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models
by: Li, Jiayu, et al.
Published: (2026)

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

Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning
by: Gao, Zhengqing, et al.
Published: (2024)

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

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

Debiased Prompt Tuning in Vision-Language Model without Annotations
by: Jiang, Chaoquan, et al.
Published: (2025)

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

SemPT: Semantic Prompt Tuning for Vision-Language Models
by: Shi, Xiao, et al.
Published: (2025)

Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
by: Jie, Shibo, et al.
Published: (2024)

TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models
by: Adhikari, Rabin, et al.
Published: (2024)