Image Conversion Model Training with Region-Focused Patch Loss
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Solution Overview
Problem
Existing image conversion models do not effectively address how the training process is influenced by the selection of positive example regions in the input image, leading to suboptimal performance in converting images between different environments.
Innovation Solution
A model training apparatus that extracts positive example patches intensively from specific regions of the input image, using a patch-wise loss function to enhance the training of an image conversion model, which includes a feature extraction model and an image generation model, to accurately convert images between different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the model uses conventional training methods without region-specific patch extraction, then the training process is simple and fast, but the model accuracy for converting specific objects between different environments is insufficient
Solution Approach 1:
The patent segments the input image into multiple partial regions (patches) and extracts positive example patches from specific regions representing objects of specific types. This segmentation allows the model to focus on and accurately convert specific objects between different environments while maintaining the overall training process structure.
Solution Approach 2:
The patent applies local quality by extracting positive example patches intensively from specific regions that represent objects of specific types. This means different regions of the image are treated differently during training - object regions are sampled with higher probability to improve accuracy for those specific objects, while other regions are treated normally.
2Reliability
If the model extracts positive example patches from all regions uniformly, then the training is balanced and simple, but the model cannot effectively focus on converting specific objects like vehicles between different environmental conditions
Solution Approach 1:
The patent implements local quality by defining a specific region representing an object of a specific type and extracting positive example patches from this region with higher probability. This allows the model to adapt its training focus to specific objects (e.g., vehicles) while maintaining the ability to handle other objects and environments through the same framework.
Solution Approach 2:
The patent introduces dynamics by making the patch extraction process adaptive - the probability of extracting a patch from a specific region can be adjusted based on the training needs. This dynamic extraction strategy allows the model to focus on specific objects when needed while maintaining versatility for other scenarios.
3Productivity
If the training process does not consider region-specific sampling, then the training is fast and straightforward, but the model fails to achieve high accuracy in converting specific objects between different environments
Solution Approach 1:
The patent applies partial action by extracting positive example patches only from specific regions representing objects of specific types, rather than uniformly from all regions. This partial extraction strategy focuses computational resources on the most important regions for improving accuracy, while the overall training process remains efficient by processing only the necessary patches.
Data Source
AI summary
The model training apparatus trains an image conversion model to generate, from an input image representing a scene in a first environment, an output image representing the scene in a second environment. The model training apparatus inputs a training image to the image conversion model to obtain a first feature map and an output image, input the output image to the image conversion model to obtain a second feature map, computes a patch-wise loss using the features corresponding to a positive example patch and a negative example patch extracted from the training image and a positive example patch extracted from the output image, and trains the image conversion model based on the patch-wise loss, which is extracted intensively from the region representing an object of a specific type.