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. Applying this to ImageNet, we construct FineGen-100K, a hierarchical dataset containing over 147,0
The increasing sophistication of AI models and the growing demand for high-quality, specialized training data make this VLM-based framework timely for advancing fine-grained perception capabilities.
This research addresses a critical bottleneck in vision-language models by automating the generation of hard negative samples, leading to more robust and accurate AI systems capable of nuanced understanding.
Dataset construction for AI model training can become significantly more efficient and effective, shifting from manual curation to automated, intelligent generation, particularly for challenging fine-grained tasks.
- · AI researchers and developers
- · Companies building advanced vision-language models
- · Industries requiring fine-grained image analysis (e.g., medical imaging, quality
- · Manual data annotation services (for certain tasks)
- · AI models reliant on less robust, older datasets
Improved performance of fine-grained vision-language models across various applications.
Acceleration of AI development in areas requiring nuanced visual understanding, potentially leading to new product categories.
Enhanced AI capabilities contributing to more sophisticated autonomous agents and quality control systems in complex environments.
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Read at arXiv cs.AI