Cover Image

Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Heo, Jaehyuk, Kang, Pilsung
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

Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions
by: Heo, Jaehyuk, et al.
Published: (2025)

Domain Adaptation of Attention Heads for Zero-shot Anomaly Detection
by: Jeong, Kiyoon, et al.
Published: (2025)

Fine-tuning Pre-trained Vision-Language Models in a Human-Annotation-Free Manner
by: Wang, Qian-Wei, et al.
Published: (2026)

Pre-Trained Vision-Language Models as Partial Annotators
by: Wang, Qian-Wei, et al.
Published: (2024)

Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
by: Woo, Sangmin, et al.
Published: (2024)

Low-hallucination Synthetic Captions for Large-Scale Vision-Language Model Pre-training
by: Zhang, Xinsong, et al.
Published: (2025)

Do Pre-trained Vision-Language Models Encode Object States?
by: Newman, Kaleb, et al.
Published: (2024)

RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
by: Woo, Sangmin, et al.
Published: (2024)

SocialFusion: Addressing Social Degradation in Pre-trained Vision-Language Models
by: Tahboub, Hamza, et al.
Published: (2025)

MAFA: Managing False Negatives for Vision-Language Pre-training
by: Byun, Jaeseok, et al.
Published: (2023)

Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models
by: Chen, Mengyuan, et al.
Published: (2024)

Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models
by: Kong, Dehong, et al.
Published: (2024)

TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models
by: Fhima, Jonathan, et al.
Published: (2024)

A Unified Understanding of Adversarial Vulnerability Regarding Unimodal Models and Vision-Language Pre-training Models
by: Zheng, Haonan, et al.
Published: (2024)

A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models
by: Chen, Wutao, et al.
Published: (2026)

Pre-trained Vision-Language Models Learn Discoverable Visual Concepts
by: Zang, Yuan, et al.
Published: (2024)

Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
by: Ye, Wei, et al.
Published: (2024)

Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
by: Liu, Che, et al.
Published: (2024)

Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models
by: Hu, Yuanwei, et al.
Published: (2026)

Superpixel Semantics Representation and Pre-training for Vision-Language Task
by: Zhang, Siyu, et al.
Published: (2023)

Grounded Knowledge-Enhanced Medical Vision-Language Pre-training for Chest X-Ray
by: Deng, Qiao, et al.
Published: (2024)

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
by: Jiang, Chaoya, et al.
Published: (2023)

3D Modality-Aware Pre-training for Vision-Language Model in MRI Multi-organ Abnormality Detection
by: Zhu, Haowen, et al.
Published: (2026)

DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset
by: Shen, Hengyu, et al.
Published: (2026)

ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
by: Zhou, Yuhao, et al.
Published: (2026)

Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective
by: Verma, Sahil, et al.
Published: (2023)

Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
by: Zheng, Weijie, et al.
Published: (2024)

MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations
by: Zhang, Ziyang, et al.
Published: (2025)

Patch Rebirth: Toward Fast and Transferable Model Inversion of Vision Transformers
by: Heo, Seongsoo, et al.
Published: (2025)

Practical Continual Forgetting for Pre-trained Vision Models
by: Zhao, Hongbo, et al.
Published: (2025)

Feedback-based Modal Mutual Search for Attacking Vision-Language Pre-training Models
by: Ding, Renhua, et al.
Published: (2024)

Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
by: Dong, Wei, et al.
Published: (2024)

PromptEcho: Annotation-Free Reward from Vision-Language Models for Text-to-Image Reinforcement Learning
by: Liu, Jinlong, et al.
Published: (2026)

Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach
by: Zong, Chen-Chen, et al.
Published: (2025)

MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training
by: Ni, Xuefeng, et al.
Published: (2024)

Compensating Distribution Drifts in Class-incremental Learning of Pre-trained Vision Transformers
by: Rao, Xuan, et al.
Published: (2025)

Pre-training Vision Transformers with Formula-driven Supervised Learning
by: Kataoka, Hirokatsu, et al.
Published: (2022)

VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessment
by: Jia, Ziheng, et al.
Published: (2025)

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
by: Li, Lin, et al.
Published: (2024)

On Pre-training of Multimodal Language Models Customized for Chart Understanding
by: Fan, Wan-Cyuan, et al.
Published: (2024)