Cross-Camera Color Constancy Using Fast Meta-Learning Adaptation
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Solution Overview
Problem
Existing approaches to color constancy, particularly in cross-camera scenarios, face challenges due to the need for large amounts of labeled data and cumbersome model transfer across different image sensors with varying spectral sensitivities, leading to reduced accuracy for outlier sensors.
Innovation Solution
A model-agnostic meta-learning (MAML) technique is employed to train a neural network using multi-domain training data, involving meta-training and fine-tuning phases to adapt the model to new image sensors, utilizing 2D log-chrominance histograms and angular loss functions to minimize the effect of illumination variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained using images captured by the same camera to learn the image-illumination mapping, then the color constancy accuracy for that specific camera is improved, but the model cannot solve the cross-camera color constancy problem and requires retraining for each new camera
Solution Approach 1:
The patent trains a single neural network model to perform color constancy for multiple different cameras simultaneously. The model learns camera-specific parameters during training and can generalize to any camera in the training set without retraining, achieving both high accuracy and cross-camera versatility. This is accomplished by incorporating camera identification and adapting the loss function to account for camera-specific characteristics.
2Adaptability or versatility
If a model is trained to learn the image-illumination mappings across multiple image sensors, then cross-camera color constancy is improved, but a large amount of labeled data from different image sensors is required which is challenging to acquire
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network on a diverse set of cameras with limited labeled data before deployment. The model learns generalizable features and camera-specific parameters in advance, so that when deployed to a new camera, it only requires minimal adaptation rather than extensive retraining with large amounts of new labeled data.
Solution Approach 2:
The patent changes parameters by learning camera-specific parameters during the training process. Instead of requiring large amounts of data for each camera, the model learns to identify and adapt to different camera characteristics through parameter adjustment, enabling efficient cross-camera generalization with limited training data per camera.
3Adaptability or versatility
If traditional transfer learning is used to transfer a learned model between different image sensors, then some cross-camera capability is achieved, but the process is complex and cumbersome
Solution Approach 1:
The patent segments the training process into distinct phases: pre-training on multiple cameras to learn general features and camera identification, and then simple fine-tuning for each specific camera. This segmentation simplifies the overall transfer process, making it less cumbersome than traditional transfer learning while maintaining cross-camera adaptability.
Solution Approach 2:
The patent introduces camera identification as an intermediary step. The model first identifies which camera is capturing the image, then applies camera-specific parameters learned during training. This intermediary mechanism simplifies the transfer process by automatically adapting to the correct camera without requiring complex manual configuration or retraining procedures.
Data Source
AI summary
Embodiments of this disclosure can provide a system and method for white balancing images. During operation, the system can obtain labeled red, green, and blue (RGB) image samples captured by a plurality of cameras and generate a plurality of training tasks. A respective training task is associated with RGB image samples captured by a corresponding camera. The system can perform meta-training over the plurality of training tasks to obtain a meta model, with parameters of the meta model optimized based on a global loss function. The system can obtain an image captured by a first camera, fine-tune the meta model using labeled RGB image samples captured by the first camera to obtain a fine-tuned model specific to the first camera, and implement the fine-tuned model to white balance the image.