CoLLaVO: Crayon Large Language and Vision mOdel

• URL: http://arxiv.org/abs/2402.11248v4
• Date: Sun, 2 Jun 2024 17:34:18 GMT
• Title: CoLLaVO: Crayon Large Language and Vision mOdel
• Authors: Byung-Kwan Lee, Beomchan Park, Chae Won Kim, Yong Man Ro,
• Abstract summary: It remains unexplored whether current Vision Language Models genuinely possess quality object-level image understanding capabilities. Our findings reveal that the image understanding capabilities of current VLMs are strongly correlated with their zero-shot performance on vision language (VL) tasks. To enhance object-level image understanding, we propose Crayon Large Language and Vision mOdel (CoLLaVO) We present a learning strategy of Dual QLoRA to preserve object-level image understanding without forgetting it during visual instruction tuning.
• Score: 42.182009352159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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