PennSyn2Real: Training Object Recognition Models without Human Labeling
This addresses the data scarcity issue for training object recognition models in computer vision, particularly for micro aerial vehicles, though it is incremental as it builds on existing synthetic data techniques.
The authors tackled the problem of scalable training data generation for deep learning by creating PennSyn2Real, a synthetic dataset of over 100,000 4K images of micro aerial vehicles, which enables training object recognition models without human labeling and shows competitive performance compared to using only real images.
Scalable training data generation is a critical problem in deep learning. We propose PennSyn2Real - a photo-realistic synthetic dataset consisting of more than 100,000 4K images of more than 20 types of micro aerial vehicles (MAVs). The dataset can be used to generate arbitrary numbers of training images for high-level computer vision tasks such as MAV detection and classification. Our data generation framework bootstraps chroma-keying, a mature cinematography technique with a motion tracking system, providing artifact-free and curated annotated images where object orientations and lighting are controlled. This framework is easy to set up and can be applied to a broad range of objects, reducing the gap between synthetic and real-world data. We show that synthetic data generated using this framework can be directly used to train CNN models for common object recognition tasks such as detection and segmentation. We demonstrate competitive performance in comparison with training using only real images. Furthermore, bootstrapping the generated synthetic data in few-shot learning can significantly improve the overall performance, reducing the number of required training data samples to achieve the desired accuracy.