Low-hallucination Synthetic Captions for Large-Scale Vision-Language Model Pre-training
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Xinsong, Zeng, Yarong, Huang, Xinting, Hu, Hu, Xie, Runquan, Hu, Han, Kang, Zhanhui |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
by: Liu, Jiazhen, et al.
Published: (2024)
by: Liu, Jiazhen, et al.
Published: (2024)
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
by: Lai, Zhengfeng, et al.
Published: (2024)
by: Lai, Zhengfeng, et al.
Published: (2024)
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model
by: Hu, Panwen, et al.
Published: (2024)
by: Hu, Panwen, et al.
Published: (2024)
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models
by: Zhang, Yudong, et al.
Published: (2025)
by: Zhang, Yudong, et al.
Published: (2025)
FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training
by: Huang, Jiale, et al.
Published: (2024)
by: Huang, Jiale, et al.
Published: (2024)
Scaling up Multimodal Pre-training for Sign Language Understanding
by: Zhou, Wengang, et al.
Published: (2024)
by: Zhou, Wengang, et al.
Published: (2024)
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training
by: Liu, Haowei, et al.
Published: (2024)
by: Liu, Haowei, et al.
Published: (2024)
Large-scale Pre-training for Grounded Video Caption Generation
by: Kazakos, Evangelos, et al.
Published: (2025)
by: Kazakos, Evangelos, et al.
Published: (2025)
Scaling Backwards: Minimal Synthetic Pre-training?
by: Nakamura, Ryo, et al.
Published: (2024)
by: Nakamura, Ryo, et al.
Published: (2024)
DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models
by: Zhang, Yudong, et al.
Published: (2024)
by: Zhang, Yudong, et al.
Published: (2024)
DreamLIP: Language-Image Pre-training with Long Captions
by: Zheng, Kecheng, et al.
Published: (2024)
by: Zheng, Kecheng, et al.
Published: (2024)
Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation
by: Zhang, Dingwen, et al.
Published: (2024)
by: Zhang, Dingwen, et al.
Published: (2024)
MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models
by: Hua, Hang, et al.
Published: (2024)
by: Hua, Hang, et al.
Published: (2024)
Muskie: Multi-view Masked Image Modeling for 3D Vision Pre-training
by: Li, Wenyu, et al.
Published: (2025)
by: Li, Wenyu, et al.
Published: (2025)
Scaling Pre-training to One Hundred Billion Data for Vision Language Models
by: Wang, Xiao, et al.
Published: (2025)
by: Wang, Xiao, et al.
Published: (2025)
Sparse Reasoning is Enough: Biological-Inspired Framework for Video Anomaly Detection with Large Pre-trained Models
by: Huang, He, et al.
Published: (2025)
by: Huang, He, et al.
Published: (2025)
LongCaptioning: Unlocking the Power of Long Video Caption Generation in Large Multimodal Models
by: Wei, Hongchen, et al.
Published: (2025)
by: Wei, Hongchen, et al.
Published: (2025)
Are Large Pre-trained Vision Language Models Effective Construction Safety Inspectors?
by: Chen, Xuezheng, et al.
Published: (2025)
by: Chen, Xuezheng, et al.
Published: (2025)
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model
by: Ma, Shuailei, et al.
Published: (2023)
by: Ma, Shuailei, et al.
Published: (2023)
Unleashing the Intrinsic Visual Representation Capability of Multimodal Large Language Models
by: Li, Hengzhuang, et al.
Published: (2025)
by: Li, Hengzhuang, et al.
Published: (2025)
UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment
by: Zhou, Hantao, et al.
Published: (2024)
by: Zhou, Hantao, et al.
Published: (2024)
CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment
by: Wang, Bo, et al.
Published: (2025)
by: Wang, Bo, et al.
Published: (2025)
MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment
by: Das, Anurag, et al.
Published: (2024)
by: Das, Anurag, et al.
Published: (2024)
3D Vision and Language Pretraining with Large-Scale Synthetic Data
by: Yang, Dejie, et al.
Published: (2024)
by: Yang, Dejie, 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)
by: Hu, Yuanwei, et al.
Published: (2026)
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models
by: Zhang, Peng-Fei, et al.
Published: (2025)
by: Zhang, Peng-Fei, et al.
Published: (2025)
Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves
by: Wu, Shihan, et al.
Published: (2024)
by: Wu, Shihan, et al.
Published: (2024)
An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training
by: Nie, Yuxiang, et al.
Published: (2025)
by: Nie, Yuxiang, et al.
Published: (2025)
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
by: Liu, Che, et al.
Published: (2024)
by: Liu, Che, et al.
Published: (2024)
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
by: Ruan, Shouwei, et al.
Published: (2024)
by: Ruan, Shouwei, et al.
Published: (2024)
LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models
by: Liu, Lu, et al.
Published: (2026)
by: Liu, Lu, et al.
Published: (2026)
DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset
by: Shen, Hengyu, et al.
Published: (2026)
by: Shen, Hengyu, et al.
Published: (2026)
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
by: Woo, Sangmin, et al.
Published: (2024)
by: Woo, Sangmin, et al.
Published: (2024)
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
by: Wang, Zeyu, et al.
Published: (2025)
by: Wang, Zeyu, et al.
Published: (2025)
Segment and Caption Anything
by: Huang, Xiaoke, et al.
Published: (2023)
by: Huang, Xiaoke, et al.
Published: (2023)
Conflict Adaptation in Vision-Language Models
by: Hu, Xiaoyang
Published: (2025)
by: Hu, Xiaoyang
Published: (2025)
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models
by: Yang, Fan, et al.
Published: (2024)
by: Yang, Fan, et al.
Published: (2024)
Industrial Synthetic Segment Pre-training
by: Mae, Shinichi, et al.
Published: (2025)
by: Mae, Shinichi, et al.
Published: (2025)
VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation
by: Zhang, Ruiyang, et al.
Published: (2024)
by: Zhang, Ruiyang, et al.
Published: (2024)
Boosting Image Restoration via Priors from Pre-trained Models
by: Xu, Xiaogang, et al.
Published: (2024)
by: Xu, Xiaogang, et al.
Published: (2024)
Similar Items
-
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
by: Liu, Jiazhen, et al.
Published: (2024) -
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
by: Lai, Zhengfeng, et al.
Published: (2024) -
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model
by: Hu, Panwen, et al.
Published: (2024) -
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models
by: Zhang, Yudong, et al.
Published: (2025) -
FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training
by: Huang, Jiale, et al.
Published: (2024)