ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee
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 (free-form) 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.
BibTeX
@inproceedings{cvpr2024_vipllavamakingla,
title = {ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts},
author = {Mu Cai and Haotian Liu and Siva Karthik Mustikovela and Gregory P. Meyer and Yuning Chai and Dennis Park and Yong Jae Lee},
booktitle = {CVPR 2024},
year = {2024}
}