no code implementations • 23 Aug 2023 • Kshitij Nikhal, Yujunrong Ma, Shuvra S. Bhattacharyya, Benjamin S. Riggan
Using our approach, more than 70% of the samples with compact hash codes exit early on the Market1501 dataset, saving 80% of the networks computational cost and improving over other hash-based methods by 60%.
no code implementations • 31 Aug 2022 • Yi-Ting Shen, Yaesop Lee, Heesung Kwon, Damon M. Conover, Shuvra S. Bhattacharyya, Nikolas Vale, Joshua D. Gray, G. Jeremy Leong, Kenneth Evensen, Frank Skirlo
Learning to detect objects, such as humans, in imagery captured by an unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused by the UAV's position towards the objects.
1 code implementation • 25 May 2020 • Renlong Hang, Zhu Li, Qingshan Liu, Pedram Ghamisi, Shuvra S. Bhattacharyya
Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.
3 code implementations • 1 Oct 2018 • Yi Zhou, Yue Bai, Shuvra S. Bhattacharyya, Heikki Huttunen
In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients.
no code implementations • 2 Jul 2018 • Yue Bai, Shuvra S. Bhattacharyya, Antti P. Happonen, Heikki Huttunen
We propose a new framework for image classification with deep neural networks.