Announcing the ORBIT dataset: Advancing real-world few-shot learning using teachable object recognition
Object recognition systems have made spectacular advances in recent years, but they rely on training datasets with thousands of high-quality, labelled examples per object category. Learning new objects from only a few examples could open the door to many new applications. For example, robotics manufacturing requires a system to quickly learn new parts, while assistive technologies need to be adapted to the unique needs and abilities of every individual.
Few-shot learning aims to reduce these demands by training models that can recognize completely novel objects from only a few examples, say 1 to 10. In particular, meta-learning algorithms—which ‘learn to learn’ using episodic training—are a promising approach to significantly reduce the number of training examples needed to train a model. However, most research in few-shot learning