apple/ml-ferret
Ml Ferret
ml-ferret is a multimodal large language model framework and visual reasoning engine designed to reason about images and user interfaces. It functions as a UI grounding model and referring expression comprehension tool that maps natural language descriptions to precise pixel coordinates.
The system focuses on high-resolution image analysis to identify and locate specific interface components. It employs multi-resolution image processing and region-aware visual encoding to preserve detail across different aspect ratios, enabling the model to analyze spatial relationships and functional layouts.
The project covers capabilities for UI layout analysis, image object grounding, and visual reasoning. These are supported by hierarchical instruction tuning and grounding evaluation benchmarks to validate the accuracy of spatial referencing and object location.
Features
- Multimodal Large Language Models - Provides a neural architecture that integrates visual encoders with language models for multimodal reasoning over images and text.
- Visual Grounding - Maps natural language descriptions to precise pixel coordinates to identify and locate specific user interface components.
- Visual Reasoning - Interprets graphical user interfaces and screenshots to perform complex logic tasks based on spatial layouts.
- Text-to-Bounding-Box Models - Implements models that convert natural language descriptions into precise spatial bounding box coordinates for UI elements.
- Multi-Resolution Visual Encoding - Implements multi-resolution image processing to preserve detail across different aspect ratios for precise UI component identification.
- Visual-Textual Alignments - Aligns visual region embeddings with linguistic tokens to enable natural language pointing to image objects.
- High-Resolution Feature Extraction - Extracts fine-grained features from high-resolution images to ensure precise identification of small UI components.
- Region-Aware Visual Reasoning - Analyzes spatial relationships and functional layouts in high-resolution images to reason about UI components.
- Interface Grounding - Maps natural language descriptions to precise pixel coordinates to identify and locate interface components.
- Multi-Resolution Visual Processing - Processes images at various scales to maintain high fidelity for small UI components while capturing the overall screen layout.
- Multimodal Reasoning Engines - Utilizes a multimodal engine to analyze spatial relationships and functional layouts for solving complex tasks.
- Region-to-Token Mappings - Maps specific image areas to discrete tokens, allowing the model to reference precise spatial coordinates within text.
- Referring Expression Comprehension - Identifies and delineates specific image regions or objects based on natural language descriptions.
- Screen Layout Analysis - Analyzes spatial arrangements and interface elements on a screen to reason about layout and function.
- Vision-Language Grounding Models - Maps natural language instructions to specific spatial bounding boxes on visual user interfaces.
- Visual Element Referencing - Identifies and describes specific components on mobile interface screens using natural language.
- Visual Coordinate Mapping - Delineates precise coordinates of objects within mobile screens to map textual references to visual areas.
- Hierarchical Instruction Sets - Uses a structured hierarchy of training tasks to evolve the model from simple object identification to complex reasoning.
- Multimodal Fine-Tuning - Optimizes large language models using hierarchical instruction datasets to improve visual grounding capabilities.
- Grounding Benchmarks - Uses standardized benchmarks to validate the accuracy of spatial referencing and object location.
