Viewpoint Invariant Object Recognition via Domain Adaptation
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Solution Overview
Problem
Current object recognition models face challenges due to the high cost and time required for generating diverse training data across various viewpoints and lighting conditions, limiting their ability to recognize objects accurately from multiple perspectives.
Innovation Solution
The method involves synthesizing multiple viewpoints and adjusting photometrics of source images using generative adversarial networks and domain adaptation techniques, allowing for efficient training with sparse data sets and improving accuracy by leveraging semi-supervised learning and unsupervised viewpoint synthesization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is collected from multiple viewpoints and lighting conditions, then object recognition accuracy is improved, but the cost and time required for data acquisition increases
Solution Approach 1:
The system performs preliminary action by synthesizing multiple viewpoint images and adjusting photometrics of source images before actual training. This pre-processing creates augmented training data that simulates various viewing conditions, eliminating the need to physically capture images from every possible angle and lighting condition, thus reducing data acquisition time while maintaining recognition accuracy
Solution Approach 2:
The system creates copies of source images through viewpoint synthesis and photometric adjustment to generate multiple augmented images from a single original. This copying approach allows the model to learn from diverse viewpoints and lighting conditions without requiring proportional increases in actual data collection, significantly reducing the time and cost of data acquisition
2Measurement precision
If training data is collected from multiple viewpoints and lighting conditions, then object recognition accuracy is improved, but the cost of obtaining training images increases
Solution Approach 1:
The system performs preliminary action by synthesizing multiple viewpoint images and adjusting photometrics of source images before actual training. This pre-processing creates augmented training data that simulates various viewing conditions, eliminating the need to physically capture images from every possible angle and lighting condition, thus reducing data acquisition time while maintaining recognition accuracy
Solution Approach 2:
The system creates copies of source images through viewpoint synthesis and photometric adjustment to generate multiple augmented images from a single original. This copying approach allows the model to learn from diverse viewpoints and lighting conditions without requiring proportional increases in actual data collection, significantly reducing the time and cost of data acquisition
3Adaptability or versatility
If domain adaptation with domain adversarial learning is applied, then viewpoint invariant recognition is achieved, but the complexity of the system increases
Solution Approach 1:
The system introduces an intermediary domain adaptation module with domain adversarial learning that acts as a mediator between the feature extractor and the classifier. This module learns to extract features that are invariant to viewpoint changes by adversarially training against domain discriminators, achieving viewpoint invariant recognition while encapsulating the complexity in a modular component that can be integrated into existing recognition pipelines
Data Source
AI summary
Systems and methods for performing domain adaptation include collecting a labeled source image having a view of an object. Viewpoints of the object in the source image are synthesized to generate view augmented source images. Photometrics of each of the viewpoints of the object are adjusted to generate lighting and view augmented source images. Features are extracted from each of the lighting and view augmented source images with a first feature extractor and from captured images captured by an image capture device with a second feature extractor. The extracted features are classified using domain adaptation with domain adversarial learning between extracted features of the captured images and extracted features of the lighting and view augmented source images. Labeled target images are displayed corresponding to each of the captured images including labels corresponding to classifications of the extracted features of the captured images.