Abhinandan Dogra

I completed my Bachelor of Technology degree in Electronics & Communication Engineering from NIT Srinagar in 2019.
Currently, I work on Computer Vision problems at Capillary Technologies in Bengaluru, India.
Previously, I completed my FYP on Image Enhancement using Generative Adversarial Networks under Prof. Ajaz H Mir and Dr. Aamir Ahsan
I was a Research Intern at Dr. S N Omkar's Lab at Indian Institute of Science, Bengaluru from Dec. 2017 - Feb. 2018.
Then I worked as a Research Intern at Froot AI based in Pune, India from May 2018 - Aug 2018.
I've also worked as a Data Science Intern at She Matters in Gurgaon from Dec. 2018 - Feb. 2019.

Email:

Google Scholar / LinkedIn / ResearchGate / GitHub

Publications

	A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild Saumya Kumaar, Abhinandan Dogra, Abrar Majeedi, Hanan Shafi, Ravi M. Vishwanath, S N Omkar IEEE International Conference on Robotics and Computer Vision, 2018 arXiv
	Disguised Facial Recognition using Neural Networks Saumya Kumaar, Ravi M. Vishwanath, S N Omkar, Abrar Majeedi, Abhinandan Dogra IEEE International Conference on Signal and Image Processing, 2018 (Oral Presentation) IEEE

Projects

	Image Enhancement using Generative Adversarial Networks Project Supervisor: Prof. AH Mir Currently working on a generative model approach for image enhancement as my FYP.
	Depth Map Estimation from single image using CNNs Built a single-image depth-map estimation system as a part of Multimedia Systems course (ECE-605). NYU Depth dataset was used for training CNN. Results and code can be found here.
	Disguised Face Recognition in the Wild Developed a state-of-the-art novel algorithm for disguised face recognition in the wild using deep CNNs. Our image annotation tool can be found here. The code for entire pipeline from classification to real-time implementation can be found in this link.
	Generative Image Inpainting with Contextual Attention A PyTorch implementation of the paper: Generative Image Inpainting with Contextual Attention. Code can be found at this link.
	Causality Detection / Policy Change Evaluation through Time-series Data A causal inference framework for policy change evaluation on time-series data. Implementation can be found at this link.

Website Repository: Here