We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| microsoft/SoM | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| highlight | Not yet run | microsoft/SoM/demo_gpt4v_som.py code served (permissive licence) · get_code("bf3338796e883021") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.11441")
get_code_for_paper("2310.11441")
have("2310.11441")
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