FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
📰 ArXiv cs.AI
Learn how FineGen, a VLM-based multi-agent framework, constructs fine-grained image-text datasets with hard negative samples, improving vision-language models
Action Steps
- Implement a VLM-based multi-agent framework using FineGen to construct image-text datasets
- Employ a Generation-Verification-Correction pipeline with closed-loop feedback to ensure semantic validity
- Synthesize hard negative samples that are strictly contradictory to visual content
- Evaluate the performance of FineGen on vision-language tasks
- Apply FineGen to real-world applications, such as image-text retrieval and visual question answering
Who Needs to Know This
Computer vision and NLP researchers can benefit from FineGen to improve their vision-language models, while data scientists and engineers can utilize the framework for automated dataset construction
Key Insight
💡 FineGen addresses the scarcity of hard negative samples in vision-language datasets, improving fine-grained perception
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🚀 FineGen: A VLM-based multi-agent framework for automated fine-grained image-text dataset construction! 📸💻
Key Takeaways
Learn how FineGen, a VLM-based multi-agent framework, constructs fine-grained image-text datasets with hard negative samples, improving vision-language models
Full Article
Title: FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
Abstract:
arXiv:2606.07645v1 Announce Type: cross Abstract: The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception. To address this, we propose FineGen, a VLM-based Multi-Agent framework for automated dataset construction. By employing a collaborative Generation-Verification-Correction pipeline with a closed-loop feedback mechanism, FineGen ensures synthesized hard negatives are semantically valid yet strictly contradictory to visual content
Abstract:
arXiv:2606.07645v1 Announce Type: cross Abstract: The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception. To address this, we propose FineGen, a VLM-based Multi-Agent framework for automated dataset construction. By employing a collaborative Generation-Verification-Correction pipeline with a closed-loop feedback mechanism, FineGen ensures synthesized hard negatives are semantically valid yet strictly contradictory to visual content
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