Object-Aware Text-to-Image Training for Multi-Object Accuracy
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Solution Overview
Problem
Existing text-to-image models struggle with accuracy in multi-object scenarios due to focusing on single-object embedding and interference from complex background information during training, leading to inaccurate model training and blurry object-background boundaries.
Innovation Solution
A text-to-image model training method using cyclic iterative training with image-text sample pairs, incorporating mask images and object class names to distinguish object regions, and constructing a loss function based on predicted noises to adjust model parameters, enhancing accuracy in multi-object scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the text-to-image model is fine-tuned based on single object embedding, then the model can focus on learning object concepts, but the accuracy in multi-object scenarios deteriorates
Solution Approach 1:
The patent segments the image into multiple object regions using mask images, allowing the model to process and learn each object separately while maintaining awareness of the complete scene. This segmentation enables the model to handle multi-object scenarios accurately by treating each object as an independent learning unit while preserving spatial relationships
Solution Approach 2:
The patent introduces a new dimension to the training process by incorporating mask images that provide spatial location information alongside object concepts. This additional dimensional information (spatial positioning) enables the model to distinguish between multiple objects in the same image, resolving the contradiction between single-object focus and multi-object accuracy
2Adaptability or versatility
If complex background information is included in the training sample, then the model can learn comprehensive scene information, but the training accuracy deteriorates due to interference
Solution Approach 1:
The patent extracts and separates object regions from the complex background using mask images. By taking out only the relevant object information while excluding distracting background elements, the model can train with high accuracy on essential object concepts without interference from complex background information, while still maintaining scene understanding capability
Solution Approach 2:
The patent applies local quality by processing different regions of the image differently - object regions are processed with high detail using mask images, while background regions are handled separately. This allows the model to focus training accuracy on object concepts while maintaining overall scene understanding through the masked region processing
Data Source
AI summary
A text-to-image model training method, apparatus, and computer-readable storage medium for enhancing text-to-image generation through object-aware training. The method trains a text-to-image model using cyclic iterative training with sample image and text pairs. Training involves selecting image-text sample pairs containing multiple objects, obtaining corresponding mask images and object class names that distinguish location regions of the objects, and inputting both the sample image with description text and the mask images with object class names into the model. The method obtains image predicted noise and object predicted noises, constructs a loss function based on these predictions, and performs parameter adjustment accordingly. This approach enables improved object-level understanding in text-to-image generation models.