Few-Shot Learning for Road Object Detection

Understand

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.