CoLLaVO: Crayon Large Language and Vision mOdel

Byung-Kwan Lee; Beomchan Park; Chae Won Kim; Yong Man Ro

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arXiv · 2402.11248

CoLLaVO: Crayon Large Language and Vision mOdel

Byung-Kwan Lee·Beomchan Park·Chae Won Kim·Yong Man Ro

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Abstract

The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet, it remains unexplored whether current VLMs genuinely possess quality object-level image understanding capabilities determined from 'what objects are in the image?' or 'which object corresponds to a specified bounding box?'. Our findings reveal that the image understanding capabilities of current VLMs are strongly correlated with their zero-shot performance on vision language (VL) tasks. This suggests that prioritizing basic image understanding is crucial for VLMs to excel at VL tasks. To enhance object-level image understanding, we propose Crayon Large Language and Vision mOdel (CoLLaVO), which incorporates instruction tuning with Crayon Prompt as a new visual prompt tuning scheme based on panoptic color maps. Furthermore, we present a learning strategy of Dual QLoRA to preserve object-level image understanding without forgetting it during visual instruction tuning, thereby achieving a significant leap in numerous VL benchmarks in a zero-shot setting.

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Byung-Kwan Lee, Beomchan Park, Chae Won Kim, Yong Man Ro. 2024-02-17. CoLLaVO: Crayon Large Language and Vision mOdel. https://arxiv.org/abs/2402.11248

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