Low-Light Human Pose Estimation via Teacher-Student Distillation
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Solution Overview
Problem
Existing methods for human pose estimation in low-light conditions face challenges due to excessive computation and low accuracy, particularly in extremely low-light scenarios, and existing datasets like NOD and ARID fail to provide sufficient training for high accuracy.
Innovation Solution
A method involving a dataset configuration using paired well-lit and low-light images, where a student model learns human pose estimation by adjusting low-light images with preset pixel values and receiving privileged information from a teacher model, utilizing separate batch normalization parameters and sharing other parameters in a neural network, and employing knowledge distillation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a low-light image is reconstructed to a well-lit image using conventional techniques, then image quality is improved, but computation becomes excessive
Solution Approach 1:
The patent introduces an intermediary approach by using a teacher-student model architecture where the teacher model processes well-lit images to generate guidance signals, and the student model processes low-light images using this guidance. This intermediary teacher model enables the system to achieve high-quality pose estimation in low-light conditions without requiring excessive computation for full image reconstruction, as the teacher model's features guide the student model's processing efficiently.
Solution Approach 2:
The patent changes the parameter space by using separate batch normalization parameters for the teacher and student models while sharing other parameters. This parameter differentiation allows the models to adapt to different lighting conditions independently while maintaining computational efficiency through parameter sharing, resolving the contradiction between image quality improvement and computation reduction.
2Ease of manufacture
If existing datasets like NOD or ARID are used for training, then training is simplified, but accuracy in extremely low-light conditions is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-processing low-light images through the student model using guidance from the teacher model before final pose estimation. This preliminary processing step enhances the feature representations in extremely low-light conditions, enabling the model to achieve high accuracy without requiring completely new dataset creation, thus maintaining training simplicity while improving performance.
Solution Approach 2:
The teacher-student architecture implements feedback mechanisms where the teacher model's processing of well-lit images provides guidance feedback to the student model processing low-light images. This feedback loop enables the student model to learn from the teacher's features and improve its performance in extremely low-light conditions using existing dataset structures, maintaining ease of training while achieving high accuracy.
3Device complexity
If the student model and teacher model use shared parameters, then model complexity is reduced, but ability to capture different lighting characteristics is limited
Solution Approach 1:
The patent applies local quality by differentiating only the batch normalization parameters between the teacher and student models while keeping other parameters shared. This localized differentiation allows each model to adapt its normalization statistics to specific lighting conditions (teacher for well-lit, student for low-light) while maintaining overall model simplicity through parameter sharing, thus resolving the contradiction between complexity and adaptability.
Data Source
AI summary
Provided is an apparatus for learning human pose estimation by configuring a dataset for human pose estimation by simultaneously obtaining a well-lit image and a low-light image, performing annotation in the well-lit image, and transmitting the annotation to the low-light image. By using the well-lit image included in the dataset as an input of a teacher model and the low-light image as an input of a student model, the student model learns human pose estimation at a high accuracy in low-light conditions by using privileged information of the teacher model.