Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

• URL: http://arxiv.org/abs/2310.11441v2
• Date: Mon, 6 Nov 2023 07:39:49 GMT
• Title: Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
• Authors: Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, Jianfeng Gao
• Abstract summary: We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models. We employ off-the-shelf interactive segmentation models, such as SEEM/SAM, to partition an image into regions, and overlay these regions with a set of marks. Using the marked image as input, GPT-4V can answer the questions that require visual grounding.
• Score: 103.68138147783614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V. As illustrated in Fig. 1 (right), we employ off-the-shelf interactive segmentation models, such as SEEM/SAM, to partition an image into regions at different levels of granularity, and overlay these regions with a set of marks e.g., alphanumerics, masks, boxes. Using the marked image as input, GPT-4V can answer the questions that require visual grounding. We perform a comprehensive empirical study to validate the effectiveness of SoM on a wide range of fine-grained vision and multimodal tasks. For example, our experiments show that GPT-4V with SoM in zero-shot setting outperforms the state-of-the-art fully-finetuned referring expression comprehension and segmentation model on RefCOCOg. Code for SoM prompting is made public at: https://github.com/microsoft/SoM.

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