ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

• URL: http://arxiv.org/abs/2312.00784v2
• Date: Sat, 27 Apr 2024 01:53:39 GMT
• Title: ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
• Authors: Mu Cai, Haotian Liu, Dennis Park, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Yong Jae Lee,
• Abstract summary: We introduce a novel multimodal model capable of decoding arbitrary visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow" Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings.
• Score: 38.59120110371588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow". Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain. Code, data, and model are publicly available.

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