Abstract: Few-shot learning is a problem of high interest in the evolution of deep
learning. In this work, we consider the problem of few-shot object detection
(FSOD) in a real-world, class-imbalanced scenario. For our experiments, we
utilize the India Driving Dataset (IDD), as it includes a class of
less-occurring road objects in the image dataset and hence provides a setup
suitable for few-shot learning. We evaluate both metric-learning and
meta-learning based FSOD methods, in two experimental settings: (i)
representative (same-domain) splits from IDD, that evaluates the ability of a
model to learn in the context of road images, and (ii) object classes with
less-occurring object samples, similar to the open-set setting in real-world.
From our experiments, we demonstrate that the metric-learning method
outperforms meta-learning on the novel classes by (i) 11.2 mAP points on the
same domain, and (ii) 1.0 mAP point on the open-set. We also show that our
extension of object classes in a real-world open dataset offers a rich ground
for few-shot learning studies.