Text-to-Mask Synthesis for Scalable Segmentation Training Data
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Solution Overview
Problem
The high cost and limited availability of high-quality human-annotated segmentation datasets hinder the development of accurate image segmentation models, as every pixel requires labor-intensive labeling, and existing datasets are insufficient for diverse training.
Innovation Solution
A combination of a text-to-mask generation model and a mask-to-image generation model is used to create synthetic datasets comprising image segmentation masks and synthesized images, leveraging text prompts to generate high-quality segmentation masks and aligned images, which are then used to train segmentation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-annotated segmentation datasets are used for training, then the quality and accuracy of training data is high, but the cost and time required for data preparation is extremely high
Solution Approach 1:
The patent uses a text-to-mask generation model to create synthetic segmentation masks that copy the essential structure and content of real images. These synthetic masks serve as substitutes for expensive human-annotated masks, maintaining training quality while eliminating the time-consuming manual labeling process. The model generates masks by processing text prompts and reference images, producing realistic segmentation results without human intervention.
Solution Approach 2:
The system enables self-service data generation where the text-to-mask model automatically creates segmentation masks without requiring human annotators. The model uses text prompts and reference images to generate masks autonomously, making the data preparation process self-sufficient and eliminating dependency on expensive human labor for mask creation.
2Adaptability or versatility
If more diverse training data is collected to improve model generalization, then the model performance on unseen data improves, but the cost and complexity of data collection increases
Solution Approach 1:
The text-to-mask generation model serves multiple functions: it generates segmentation masks for training, creates diverse synthetic data across different scenarios, and adapts to various image types and domains. A single model architecture handles diverse data generation needs by processing different text prompts and reference images, eliminating the need for separate data collection processes for each scenario.
Solution Approach 2:
The system achieves data diversity by changing parameters in text prompts and reference images fed to the generation model. By varying textual descriptions and input images, the model generates diverse segmentation masks covering different objects, scenes, and conditions, providing comprehensive training data without complex multi-source data collection.
3Productivity
If synthetic data generation models are used to reduce annotation costs, then the cost and time of data preparation decreases, but the quality and realism of generated data may be insufficient
Solution Approach 1:
The patent introduces text prompts as an intermediary between the generation model and the final segmentation masks. This intermediary layer allows for precise control over mask generation quality and characteristics. The text prompts guide the model to produce more accurate and realistic segmentation results, bridging the gap between automated generation and high-quality output that would otherwise require manual annotation.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for data generation include obtaining a text prompt describing an object within a scene and generating, using a text-to-mask generation model and based on the text prompt, a color map corresponding to the scene. The color map indicates a region corresponding to the object from the text prompt. An image segmentation mask is generated based on the color map. The image segmentation mask comprises a plurality of regions corresponding to a plurality of image elements in the scene including the region corresponding to the object from the text prompt.