Cover Image

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cheng, Cheng, Song, Lin, Xue, Ruoyi, Wang, Hang, Sun, Hongbin, Ge, Yixiao, Shan, Ying
Format:	Preprint
Published:	2023
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

Attn-Adapter: Attention Is All You Need for Online Few-shot Learner of Vision-Language Model
by: Bui, Phuoc-Nguyen, et al.
Published: (2025)

ST-LLM: Large Language Models Are Effective Temporal Learners
by: Liu, Ruyang, et al.
Published: (2024)

Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners
by: He, Xuehai, et al.
Published: (2023)

Making Large Vision Language Models to be Good Few-shot Learners
by: Liu, Fan, et al.
Published: (2024)

BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
by: Liu, Ruyang, et al.
Published: (2023)

YOLO-World: Real-Time Open-Vocabulary Object Detection
by: Cheng, Tianheng, et al.
Published: (2024)

AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction
by: Cheng, Junhao, et al.
Published: (2025)

Few-shot Learner Parameterization by Diffusion Time-steps
by: Yue, Zhongqi, et al.
Published: (2024)

Activating Wider Areas in Image Super-Resolution
by: Cheng, Cheng, et al.
Published: (2024)

From Prediction to Perfection: Introducing Refinement to Autoregressive Image Generation
by: Cheng, Cheng, et al.
Published: (2025)

GenHancer: Imperfect Generative Models are Secretly Strong Vision-Centric Enhancers
by: Ma, Shijie, et al.
Published: (2025)

DiCoDe: Diffusion-Compressed Deep Tokens for Autoregressive Video Generation with Language Models
by: Li, Yizhuo, et al.
Published: (2024)

Few-shot Adaptation of Medical Vision-Language Models
by: Shakeri, Fereshteh, et al.
Published: (2024)

Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
by: Park, Keon-Hee, et al.
Published: (2024)

Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning?
by: Cheng, Junhao, et al.
Published: (2025)

A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models
by: Huang, Hao, et al.
Published: (2025)

Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
by: An, Zhaochong, et al.
Published: (2025)

SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
by: Li, Bohao, et al.
Published: (2024)

Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition
by: Cao, Congqi, et al.
Published: (2024)

D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
by: Pei, Wenjie, et al.
Published: (2023)

What Makes Good Few-shot Examples for Vision-Language Models?
by: Guo, Zhaojun, et al.
Published: (2024)

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation
by: Ge, Yuying, et al.
Published: (2024)

Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models
by: Ali, Eman, et al.
Published: (2023)

SEED-Story: Multimodal Long Story Generation with Large Language Model
by: Yang, Shuai, et al.
Published: (2024)

FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model
by: Li, Yuanwei, et al.
Published: (2024)

OFL-SAM2: Prompt SAM2 with Online Few-shot Learner for Efficient Medical Image Segmentation
by: Lan, Meng, et al.
Published: (2025)

Test-time Alignment-Enhanced Adapter for Vision-Language Models
by: Tong, Baoshun, et al.
Published: (2024)

E2MPL:An Enduring and Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation
by: Yang, Wanqi, et al.
Published: (2024)

Vision and Language Reference Prompt into SAM for Few-shot Segmentation
by: Sakurai, Kosuke, et al.
Published: (2025)

Hierarchical Process Reward Models are Symbolic Vision Learners
by: Zhang, Shan, et al.
Published: (2025)

Benchmarking Vision-Language and Multimodal Large Language Models in Zero-shot and Few-shot Scenarios: A study on Christian Iconography
by: Spinaci, Gianmarco, et al.
Published: (2025)

Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection
by: Chen, Xinyi, et al.
Published: (2024)

Few-shot Online Anomaly Detection and Segmentation
by: Wei, Shenxing, et al.
Published: (2024)

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models
by: Qin, Mengxin, et al.
Published: (2026)

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation
by: Liu, Jie, et al.
Published: (2025)

EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios
by: Qiu, Lu, et al.
Published: (2024)

Selective Vision-Language Subspace Projection for Few-shot CLIP
by: Zhu, Xingyu, et al.
Published: (2024)

HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation
by: Xiao, Yicheng, et al.
Published: (2025)

SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing
by: Ge, Yuying, et al.
Published: (2024)

DiffCLIP: Few-shot Language-driven Multimodal Classifier
by: Zhang, Jiaqing, et al.
Published: (2024)