On Generalization of Detection Models for Unconstrained Environments

Published in ICCV workshop, 2019

Abstract:

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 us- ing 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.

This paper was accepted at ICCV workshop (AutoNUE) 2019.

If you find this work useful in your research, consider citing it,

@InProceedings{Bhargava_2019_ICCV,
author = {Bhargava, Prajjwal},
title = {On Generalizing Detection Models for Unconstrained Environments},
booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}