Multimodal synergistic interface control scheme
Large models (e.g. GPT-4V) have problems such as inaccurate operation positioning and missing steps when dealing with interface screenshots alone, which OmniParser improves with the following architecture:
- Structured middle layer:Convert screenshot to JSON tree with element coordinates, type, and state
- Multi-model pipelines:Detection model → description model → hierarchical processing of control command generation
- Windows 11 Sandbox:Provide real operating environments to verify the feasibility of the commands
Implementation of recommendations:
- Ensure that the three weighting submodules (detect/caption/florence) are downloaded in full during installation
- Test parsing in Gradio Demo before docking to LLM
- Adding Confidence Threshold Filtering to Key Operational Elements
This solution improves the accuracy of operation command generation from 63% to 89%, which is especially effective for complex controls such as drop-down menus.
This answer comes from the articleOmniParser: user interface screenshots parsed into structured elements for easy understanding and manipulation by large modelsThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of large models for generating interface operation commands?
