Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
TL;DR
Current vision-language datasets lack hard negative samples, which are essential for fine-grained perception. FineGen is a VLM-based Multi-Agent framework that automates dataset construction using a Generation-Verification-Correction pipeline.
✦ Why It Matters
Engineers can leverage FineGen to improve dataset quality for training vision-language models, enhancing their performance.
Key Takeaways
- Utilize FineGen for automated dataset construction.
- Incorporate hard negative samples to improve model training.
- Leverage FineGen-100K for enhanced performance on fine-grained tasks.
- Explore the Generation-Verification-Correction pipeline for dataset generation.
How It Works
FineGen operates through a multi-agent framework that generates hard negative samples by first creating potential candidates, then verifying their semantic validity against the visual content, and finally correcting any discrepancies. This iterative process ensures that the generated samples are not only challenging but also relevant, thereby enriching the dataset for training models.