@article{Garg_Mandal_Narang_2021, title={Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement (Student Abstract)}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17887}, DOI={10.1609/aaai.v35i18.17887}, abstractNote={Low light conditions in aerial images adversely affect the performance of several vision based applications. There is a need for methods that can efficiently remove the low light attributes and assist in the performance of key vision tasks. In this work, we propose a new method that is capable of enhancing the low light image in a self-supervised fashion, and sequentially apply detection and segmentation tasks in an end-to-end manner. The proposed method occupies a very small overhead in terms of memory and computational power over the original algorithm and delivers superior results. Additionally, we propose the generation of a new low light aerial dataset using GANs, which can be used to evaluate vision based networks for similar adverse conditions.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Garg, Prateek and Mandal, Murari and Narang, Pratik}, year={2021}, month={May}, pages={15781-15782} }