{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/making-large-multimodal-models-understand","title":"ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts","arxiv_id":"2312.00784","date":"2023-12-01","proceeding":"CVPR 2024 1","authors":["Mu Cai","Haotian Liu","Dennis Park","Siva Karthik Mustikovela","Gregory P. Meyer","Yuning Chai","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 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.","url_abs":"https://arxiv.org/abs/2312.00784v2","url_pdf":"https://arxiv.org/pdf/2312.00784v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"making-large-multimodal-models-understand","repo_url":"https://github.com/MS-P3/code7/tree/main/vipllava","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"making-large-multimodal-models-understand","repo_url":"https://github.com/MindCode-4/code-1/tree/main/vipllava","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"making-large-multimodal-models-understand","repo_url":"https://github.com/MindCode-4/code-5/tree/main/vipllava","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"making-large-multimodal-models-understand","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/5/vipllava","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"visual-commonsense-reasoning","task_name":"Visual Commonsense Reasoning"},{"task_slug":"visual-prompting","task_name":"Visual Prompting"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[{"slug":"vip-bench","name":"ViP-Bench","full_name":"Making Large Multimodal Models Understand Arbitrary Visual Prompts"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2312.00784","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}