The study introduces “Vary,” a method to expand the vision vocabulary in Large Vision-Language Models (LVLMs) for enhanced perception tasks. This method aims to improve fine-grained perception, particularly in document-level OCR and chart understanding. Experimental results demonstrate Vary’s effectiveness, outperforming other LVLMs in certain tasks. For more information, visit the Paper and Project.
Introducing Vary: Enhancing Large Vision-Language Models for Specialized Tasks
Addressing Challenges in Vision-Language Models
Large Vision-Language Models (LVLMs) have shown impressive progress in various applications, but they still face challenges in specialized tasks that demand fine-grained perception of visual content.
The Vary Method: Enhancing LVLMs for Specialized Tasks
Researchers have introduced Vary, a method that empowers LVLMs to efficiently acquire new features, improving fine-grained perception. Vary demonstrates effectiveness across functions and offers potential for further exploration, expanding LVLM capabilities while maintaining the original ones.
Two Configurations of Vary
Vary introduces two configurations: Vary-tiny and Vary-base, both focusing on enhancing fine-grained perception in tasks such as document-level OCR and chart understanding.
Performance and Key Takeaways
Vary demonstrates promising performance across multiple tasks, excelling in document-level OCR, chart understanding, and MMVet tasks. The method outperforms other LVLMs in document parsing features.
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