Viraj Prabhu

I am a fourth year CS Ph.D. student at Georgia Tech, advised by Judy Hoffman. My research interests are in developing data-efficient and resilient computer vision systems that can be deployed in the real world. Specifically, I am interested in label-efficient learning (particularly few-shot and active learning), adaptation across visual tasks and domains, and reliable and calibrated uncertainty estimation from deep neural networks.

I earned my Master's in CS (awarded the MS Research award) in Spring '19, also at Georgia Tech, where I was advised by Devi Parikh (and worked closely with Dhruv Batra) on developing visual conversational agents.

In grad school, I've had the opportunity to work on domain adaptation research at NVIDIA (with Sanja Fidler) and Salesforce (with Nikhil Naik). I also spent two wonderful summers doing research in machine learning for healthcare at Curai (with Anitha Kannan). Prior to joining Georgia Tech, I was a research assistant in the Machine Learning and Perception lab at Virginia Tech, advised by Dhruv Batra. Before that, I worked as a software developer at Adobe.

I received my Bachelor's degree in Computer Science from BITS Pilani. Over the course of my undergrad, I was fortunate to undertake research internships at Adobe, Tonbo Imaging, and CEERI Pilani, where I worked on problems ranging from video segmentation to camera calibration.

On the side, I have been an open-source contributor to CloudCV and served as mentor for Google Summer of Code (2016, 2017) and Google Code-In (2017) for the Fabrik project. I've also won a couple of hackathons (VTHacks '17, BITS Google Hackathon '14). Apart from work, I enjoy running, soccer, reading, playing the guitar, and (occasionally) writing.

Contact: I'm always happy to discuss research or grad school applications/life! Feel free to email me at

Fall '11-Spring '15
Fall '17-current
Summer '14, Fall '15- Summer '16
Summer '18, '19
Summer '21
Summer '22


Read More

Read Less


LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images

Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay, Judy Hoffman

Paper Project Page
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting

Viraj Prabhu, David Acuna, Andrew Liao, Rafid Mahmood, Marc T. Law, Judy Hoffman, Sanja Fidler, James Lucas

Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency

NeurIPS 2022

Viraj Prabhu*, Sriram Yenamandra*, Aaditya Singh, Judy Hoffman
(* = equal contribution)

Paper News
Can domain adaptation make object recognition work for everyone?

L3D-IVU workshop at CVPR 2022

Viraj Prabhu, Ramprasaath R. Selvaraju, Judy Hoffman, Nikhil Naik

AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation

Computer Vision in the Wild workshop, ECCV 2022 (spotlight)

Viraj Prabhu*, Shivam Khare*, Deeksha Kartik, Judy Hoffman
(* = equal contribution)

Paper Video
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models

BMVC 2021

Arvind Krishnakumar, Viraj Prabhu, Sruthi Sudhakar, Judy Hoffman

Paper Code
Mitigating Bias in Visual Transformers via Targeted Alignment

BMVC 2021

Sruthi Sudhakar, Viraj Prabhu, Arvind Krishnakumar, Judy Hoffman

Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation

ICCV 2021

Viraj Prabhu, Shivam Khare, Deeksha Kartik, Judy Hoffman

Paper Project Page Code Video Slides Poster
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings

ICCV 2021

Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, Judy Hoffman

Paper Project Page Code Video Slides Poster
Open Set Medical Diagnosis

ML4H workshop at NeurIPS 2019

Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David Sontag, Xavier Amatriain

Few-shot Learning for Dermatological Disease Diagnosis

MLHC 2019 (spotlight), ML4H workshop at NeurIPS 2018

Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain

Do Explanations make VQA Models more Predictable to a Human?

EMNLP 2018, Chalearn Looking at People Workshop, CVPR 2017

Arjun Chandrasekaran*, Viraj Prabhu*, Deshraj Yadav*, Prithvijit Chattopadhyay*, Devi Parikh
(* = equal contribution)

The Promise of Premise: Harnessing Question Premises in Visual Question Answering

EMNLP 2017

Aroma Mahendru*, Viraj Prabhu*, Akrit Mohapatra*, Dhruv Batra, Stefan Lee
(* equal contribution)

Paper Code Dataset
Evaluating Visual Conversational Agents via Cooperative Human-AI Games

HCOMP 2017

Prithvijit Chattopadhyay*, Deshraj Yadav*, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh

Paper Code


Fabrik: An Online Collaborative Neural Network Editor

Utsav Garg, Viraj Prabhu, Deshraj Yadav, Ram Ramrakhya, Harsh Agrawal, Dhruv Batra

Lead mentor on Fabrik, an open-source web platform to collaboratively build, visualize, and design neural networks in the browser.

Report Code
PyTorch implementation of Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

Nirbhay Modhe, Viraj Prabhu, Michael Cogswell, Satwik Kottur, Abhishek Das, Stefan Lee, Devi Parikh, Dhruv Batra

Adobe Captivate Prime

During my time as a software developer at Adobe (Aug '15-'16), I was responsible for the Captivate Prime Android app through two release cycles. I developed features for offline content play-back, syncing, and UI.

Automated camera calibration and boresighting

Over a research internship at Tonbo Imaging (Spring '15), I designed an algorithm for automated camera calibration and implemented a boresighting algorithm for the company's video precision boresight tool.


Over an internship at Adobe, I co-developed KeyframeCut, a fast graphcut-based segmentation algorithm for real-time background substitution in video. Transferred into Magic Green Screen, the marquee feature of Adobe Presenter Video Express 11.

Demo Blog