F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning

TL;DR

This paper introduces F-SIOL-310, a new robotic dataset and benchmark designed to evaluate few-shot incremental object learning, highlighting the challenges and current limitations of existing methods in robotic vision tasks.

Contribution

The paper presents a novel dataset and benchmark specifically for few-shot incremental object learning in robotics, filling a significant gap in existing resources.

Findings

Current incremental learning algorithms perform poorly on F-SIOL-310.

Few-shot incremental object learning remains a challenging problem.

Benchmark results highlight the need for improved methods.

Abstract

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.