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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 12914-12923

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.