Visual Language Model Training for Graphical Element Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models struggle to accurately identify and locate image elements within graphical images, such as charts and diagrams, limiting their performance in tasks involving complex documents with both text and graphical data.
Innovation Solution
A visual language model is trained using a system that generates training data items comprising graphical images and natural language queries, adjusting parameters to optimize the model's ability to predict the locations of image elements within these images, improving its performance in recognizing characters and extracting information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are used to identify image elements in graphical images, then the model structure remains simple, but the location identification accuracy deteriorates
Solution Approach 1:
The patent segments the image element identification task into multiple sub-tasks: graphical element detection, text element detection, and location prediction. Each sub-task is handled by specialized components within the visual language model, allowing the model to focus on specific features (geometric properties for graphical elements, character recognition for text) independently, thereby improving overall location identification accuracy without requiring a completely new model architecture
Solution Approach 2:
The visual language model is designed as a universal model that can handle multiple types of image elements (graphical elements like lines, shapes, and text elements) within a single integrated architecture. The model uses a unified parameter set that can be adapted to detect different element types through prompt engineering and multi-task training, improving location accuracy across diverse graphical images without requiring separate specialized models for each element type
2Adaptability or versatility
If the visual language model is trained on diverse graphical images, then the adaptability improves, but the training data requirements and processing complexity increase
Solution Approach 1:
The patent employs parameter changes in the form of adaptive prompt tuning and dynamic parameter adjustment during training. The visual language model uses learnable parameters that can be adjusted based on the specific graphical image type being processed. The training process incorporates parameter adaptation mechanisms that allow the model to automatically adjust its internal parameters for different element types and graphical contexts, improving adaptability while managing training complexity through efficient parameter optimization
Solution Approach 2:
The training process incorporates preliminary actions such as pre-processing graphical images to extract feature representations, pre-segmenting images into potential element regions, and pre-generating ground truth annotations for location data. These preliminary actions prepare the data and model components in advance, reducing the complexity of the main training process and enabling the model to handle diverse graphical image types more efficiently
Data Source
AI summary
A method performed by one or more computers and for training a visual language model to identify locations of image elements within an image. The method comprises: generating a plurality of training data items, each training data item including (i) an image rendered according to a corresponding set of instructions, (ii) a natural language query for identifying at least one image element of the image, and (iii) a target location for the at least one image element, the target location being determined from the set of instructions. The method further comprises, for each of the training data items, processing the corresponding image and natural language query using a visual language model to generate a corresponding model output comprising a predicted location of an image element identified from the natural language query.