Image-Text Embedding Color Augmentation for Precise Color Retrieval
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Solution Overview
Problem
Existing image-text embedding models struggle to accurately understand precise colors, particularly when colors exhibit subtle resemblances, leading to performance issues in image retrieval tasks and downstream applications.
Innovation Solution
A method that enhances color comprehension by modifying initial images to depict different colors and training the model with curated image-text pairs, incorporating hard negative images, and applying text and image prior losses to preserve semantic context and mitigate overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-text embedding models are trained on existing datasets with broader color terms, then the model achieves general color understanding, but the model fails to accurately understand precise colors with subtle resemblances
Solution Approach 1:
The patent applies preliminary action by generating synthetic image-text pairs with precise color specifications before training the model. The method creates augmented datasets with hard negative examples that explicitly depict subtle color distinctions, allowing the model to learn precise color boundaries before encountering real-world retrieval tasks. This pre-training on carefully constructed color-discriminative data resolves the contradiction by establishing precise color understanding foundations.
Solution Approach 2:
The patent changes the color parameter specifications in training data from broad color terms to precise RGB values with controlled variations. By systematically varying color parameters in synthetic images and creating hard negative examples with subtle color differences, the model learns to distinguish between similar colors. This parameter transformation enables the model to achieve both precise color understanding and reliable image retrieval performance.
2Measurement precision
If the model is directly fine-tuned for color understanding, then the model may achieve better color discrimination, but the model encounters overfitting and mode-collapse due to limited availability of image-text pairs with precise colors
Solution Approach 1:
The patent applies copying by generating synthetic image-text pairs that replicate real-world scenarios with precise color specifications. Instead of relying on limited real datasets, the method creates artificial copies of images with modified color values and corresponding text descriptions. These synthetic training examples enable the model to learn precise color discrimination without requiring extensive real-world annotated data, thus avoiding overfitting while improving color discrimination capability.
Solution Approach 2:
The patent transforms the training approach by changing from using existing broad-color datasets to generating synthetic data with precise RGB color parameters. By systematically varying color parameters and creating hard negative examples with subtle differences, the method enriches the training data without increasing data collection complexity. This parameter-based data generation resolves the contradiction between improving color discrimination and managing training data requirements.
3Adaptability or versatility
If the model prioritizes broader color terms in training, then the model achieves robust general color recognition, but the model cannot accurately retrieve images with exact RGB color specifications
Solution Approach 1:
The patent applies segmentation by separating the color understanding capability into two distinct processing paths: one for general color terms and another for precise RGB specifications. The model learns to handle broad color categories (red, blue, green) separately from exact color values, allowing each path to be optimized independently. This segmentation enables the model to maintain adaptability for general color recognition while achieving manufacturing precision for exact color retrieval tasks.
Solution Approach 2:
The patent changes the granularity of color parameter representation in training data, creating a hierarchy from coarse color categories to fine RGB specifications. By incorporating both broad color terms and precise color values in synthetic training pairs, the model learns to adapt its color understanding precision based on the required output granularity. This parameter hierarchy resolves the contradiction by enabling the model to operate effectively at both general and precise color recognition levels.
Data Source
AI summary
Provided is a method that operates to enhance the color understanding capabilities of an image-text embedding model. The proposed approach can include modifying an initial image depicting an object of a certain color to generate a modified image where the object has a different color. This can be done by adjusting the color values of the pixels in the initial image. For example, an image of a red apple can be modified to depict a green apple. The technology then trains an image-text embedding model using this modified image and a text prompt that describes the modified image.