  Deep learning has achieved remarkable success in object recognition tasks
through the availability of large scale datasets like ImageNet. However, deep
learning systems suffer from catastrophic forgetting when learning
incrementally without replaying old data. For real-world applications, robots
also need to incrementally learn new objects. Further, since robots have
limited human assistance available, they must learn from only a few examples.
However, very few object recognition datasets and benchmarks exist to test
incremental learning capability for robotic vision. Further, there is no
dataset or benchmark specifically designed for incremental object learning from
a few examples. To fill this gap, we present a new dataset termed F-SIOL-310
(Few-Shot Incremental Object Learning) which is specifically captured for
testing few-shot incremental object learning capability for robotic vision. We
also provide benchmarks and evaluations of 8 incremental learning algorithms on
F-SIOL-310 for future comparisons. Our results demonstrate that the few-shot
incremental object learning problem for robotic vision is far from being
solved.
