AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
📰 ArXiv cs.AI
Learn how to automate vision data cleaning using vision-language models with AutoVDC, improving dataset quality for autonomous driving systems
Action Steps
- Apply vision-language models to detect annotation errors in datasets
- Configure AutoVDC framework to automate data cleaning process
- Test AutoVDC on a sample dataset to evaluate its performance
- Run AutoVDC on a large-scale dataset to improve data quality
- Compare the results of AutoVDC with manual annotation methods to measure its effectiveness
Who Needs to Know This
Data scientists and engineers working on autonomous driving systems can benefit from AutoVDC to improve dataset quality and reduce manual annotation efforts
Key Insight
💡 Vision-language models can be used to automate vision data cleaning, reducing manual annotation efforts and improving dataset quality
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🚀 AutoVDC: Automated Vision Data Cleaning using Vision-Language Models! 📸💻
Key Takeaways
Learn how to automate vision data cleaning using vision-language models with AutoVDC, improving dataset quality for autonomous driving systems
Full Article
Title: AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
Abstract:
arXiv:2507.12414v2 Announce Type: replace-cross Abstract: Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple iterations are often needed to produce high-quality datasets. However, manually reviewing large datasets is laborious and expensive. In this paper, we introduce AutoVDC (Automated Vision Data Cleaning) framework and investigate the utilization of Vision-Language
Abstract:
arXiv:2507.12414v2 Announce Type: replace-cross Abstract: Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple iterations are often needed to produce high-quality datasets. However, manually reviewing large datasets is laborious and expensive. In this paper, we introduce AutoVDC (Automated Vision Data Cleaning) framework and investigate the utilization of Vision-Language
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