MLLM by Motiff leverages a classic mixture-of-experts approach, linking pre-trained vision encoders with a large language model (LLM) through connectors. The workflow is as follows:
- Visual Processing: Images are processed by a vision encoder and transformed into visual tokens by the vision-language connector.
- Text Generation: The visual tokens are combined with text tokens, allowing the LLM to generate comprehensive text responses, enhancing UI design interaction.
Due to the scarcity of high-quality UI domain data, we employed the following methods for data collection:
- UI Screenshot Descriptions: Detailed modular descriptions of UI screenshots, covering layouts, components, and functionalities.
- Structured UI Descriptions: Focus on high-quality, knowledge-dense data, precisely identifying and describing UI components.
- UI Task Tuning Data: Constructed a comprehensive set of UI-related tasks, including descriptions, Q&A, pixel-level positioning, and interaction guides.
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Due to the scarcity of high-quality UI domain data, we employed the following methods for data collection:
- UI Screenshot Descriptions: Detailed modular descriptions of UI screenshots, covering layouts, components, and functionalities. - Structured UI Descriptions: Focus on high-quality, knowledge-dense data, precisely identifying and describing UI components. - UI Task Tuning Data: Constructed a comprehensive set of UI-related tasks, including descriptions, Q&A, pixel-level positioning, and interaction guides.