Neural Network Keypoint Disentanglement for Unsupervised Pose Prediction
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Solution Overview
Problem
Pose prediction in computer vision requires expensive pixel-level annotations for object parts, making the training process costly and labor-intensive due to the need for human supervision.
Innovation Solution
The use of neural networks that learn semantic keypoint-like representations without expensive keypoint-level annotations by disentangling input data into foreground and background components, allowing for unsupervised training and reconstruction of keypoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-level annotations are used for training pose prediction models, then measurement precision is improved, but loss of time and manufacturing cost increase due to human supervision requirements
Solution Approach 1:
The system uses self-supervised learning where the neural network automatically generates its own training data by predicting keypoint annotations from images without requiring manual annotations. The model trains by reconstructing images from predicted keypoints, allowing the system to serve itself rather than requiring human annotators.
Solution Approach 2:
The patent creates synthetic annotated data by copying and transforming unlabeled images. The neural network generates fake annotations by predicting keypoint locations and using these to create synthetic labeled images that mimic real annotated data, allowing the model to learn from synthetic copies rather than real manual annotations.
2Manufacturing precision
If pixel-level annotations are provided for training data, then manufacturing precision is improved, but device complexity and cost increase due to human supervision requirements
Solution Approach 1:
The system eliminates the need for complex manual annotation processes by implementing self-supervised learning. The neural network automatically generates annotations and training data through image reconstruction tasks, simplifying the overall training system by removing human supervision infrastructure.
Solution Approach 2:
The patent replaces the mechanical process of manual human annotation with an automated neural network-based system. Instead of requiring human experts to manually mark keypoints, the system uses deep learning models to automatically predict and generate annotations through computational processes.
3Loss of time
If unsupervised learning without annotations is used, then loss of time is reduced, but measurement precision deteriorates due to lack of ground truth data
Solution Approach 1:
The system implements feedback loops where the neural network predicts keypoints, reconstructs images from these predictions, and uses the reconstruction error as feedback to refine future predictions. This iterative feedback process gradually improves prediction accuracy without requiring initial ground truth annotations.
Solution Approach 2:
The patent creates synthetic annotated data by copying and transforming unlabeled images. The neural network generates fake annotations by predicting keypoint locations and using these to create synthetic labeled images that mimic real annotated data, allowing the model to learn from synthetic copies rather than real manual annotations.
4Manufacturing precision
If manual annotation of object parts is performed, then manufacturing precision is improved, but productivity decreases due to labor-intensive process
Solution Approach 1:
The system uses self-supervised learning where the neural network automatically generates its own training data by predicting keypoint annotations from images without requiring manual annotations. The model trains by reconstructing images from predicted keypoints, allowing the system to serve itself rather than requiring human annotators.
Solution Approach 2:
The patent replaces the mechanical process of manual human annotation with an automated neural network-based system. Instead of requiring human experts to manually mark keypoints, the system uses deep learning models to automatically predict and generate annotations through computational processes.
Data Source
AI summary
Apparatuses, systems, and techniques to perform unsupervised keypoint or landmark learning using one or more neural networks. In at least one embodiment, one or more neural networks use pose and appearance information to construct a foreground and a background, which are then used to reconstruct an input image and determine loss values to train the one or more neural networks.