Akrit Mohapatra

I am an M.S. student at the ECE department at Virginia Tech. I am a member of the Machine Learning and Perception (MLP) Lab led by Prof. Dhruv Batra and work closely with Prof. Devi Parikh.

I received my bachelors degree in Computer Engineering from Virginia Tech in 2016.

Email  /  CV


My research lies in the fields of deep learning, computer vision and natural language processing. My primary focus has been on improving models for the task of Visual Question Answering (VQA).

Towards Transparent AI Systems: Interpreting Visual Question Answering Models
Yash Goyal, Akrit Mohapatra, Devi Parikh, Dhruv Batra

International Conference on Machine Learning (ICML) Workshop on Visualization for Deep Learning, 2016
[Best Student Paper]
Interactive Visualizations: Question and Image

In this paper, we experimented with two visualization methods -- guided backpropagation and occlusion -- to interpret deep learning models for the task of Visual Question Answering. Specifically, we find what part of the input (pixels in images or words in questions) the VQA model focuses on while answering a question about an image.

CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service
Harsh Agrawal, Clint Solomon Mathialagan, Yash Goyal, Neelima Chavali, Prakriti Banik, Akrit Mohapatra, Ahmed Osman, Dhruv Batra

Book Chapter, Mobile Cloud Visual Media Computing
Editors: Gang Hua, Xian-Sheng Hua. Springer, 2015.

We present a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs.

Course Projects

Exploring Nearest Neighbor Approach on VQA
Fall 2015: ECE 5554/4984 Computer Vision by Prof. Devi Parikh


Fall 2016: ECE 4554/5554: Computer Vision Fall 2016
Teaching Assistant
Instructor: Prof. Jia-Bin Huang

Other Projects

VQA Visualization

Interpreting Visual Question Answering Models (Image side visualizations)

Interpreting Visual Question Answering Models (Question side visualizations)

Bibtex to JS
Modified bibtex-js. Upload the respective .bib file and the website renders the publications in html format.

[Website courtesy]: The website template is based on Jon Barron's website.