We presentSet-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 markse.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 outperforms the state-of-the-art fully-finetuned referring expression comprehension and segmentation model on RefCOCOg in a zero-shot setting. Code forSoMprompting is made public here:https://github.com/microsoft/SoM.