  Object detection has seen tremendous progress in recent years. However,
current algorithms don't generalize well when tested on diverse data
distributions. We address the problem of incremental learning in object
detection on the India Driving Dataset (IDD). Our approach involves using
multiple domain-specific classifiers and effective transfer learning techniques
focussed on avoiding catastrophic forgetting. We evaluate our approach on the
IDD and BDD100K dataset. Results show the effectiveness of our domain adaptive
approach in the case of domain shifts in environments.
