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Viraj Prabhu
2011-2015
2017-2023
Summer 2014
2015-2016
Summer 2018, 2019
Summer 2021
Jan 2024-current
Summer 2022
Viraj Prabhu
I am a research scientist on the multimodal AI team at Salesforce Research. I received my PhD in Computer Science from Georgia Tech in December 2023, where I was advised by Judy Hoffman and worked on making computer vision models generalize to new environments. I earned my Master's in CS (awarded the MS Research award) in May 2019, also at Georgia Tech, where I was advised by Devi Parikh and worked on developing visual conversational agents.
In grad school, I've had the opportunity to intern at NVIDIA (with Sanja Fidler), Salesforce (with Nikhil Naik), and Curai (with Anitha Kannan). Before that, I've had stints as a research assistant at Virginia Tech (with Dhruv Batra), a software engineer at Adobe, and a mentor for Google Summer of Code. I received my Bachelor's degree in Computer Science from BITS Pilani in 2015.
In my free time, I enjoy reading, running, soccer, and playing the guitar.
2011-2015
2017-2023
Summer 2014 2015-2016
Summer 2018, 2019
Summer 2021 Jan 2024-current
Summer 2022
Research
My current research focus is on developing multimodal digital agents that can perceive, reason, and act reliably in complex environments to accomplish complex goals. Representative publications are highlighted.
WALT: Web Agents that Learn Tools
Viraj Prabhu, Yutong Dai, Matthew Fernandez, Jing Gu, Krithika Ramakrishnan, Yanqi Luo, Silvio Savarese, Caiming Xiong, Junnan Li, Zeyuan Chen, Ran Xu
SCUBA: Salesforce Computer Use Benchmark
Yutong Dai, Krithika Ramakrishnan, Jing Gu, Matthew Fernandez, Yanqi Luo, Viraj Prabhu, Zhenyu Hu, Silvio Savarese, Caiming Xiong, Zeyuan Chen, Ran Xu
CoAct-1: Computer-using Agents with Coding as Actions
Linxin Song, Yutong Dai, Viraj Prabhu, Jieyu Zhang, Taiwei Shi, Li Li, Junnan Li, Silvio Savarese, Zeyuan Chen, Jieyu Zhao, Ran Xu, Caiming Xiong
Trust but Verify: Programmatic VLM Evaluation in the Wild
Viraj Prabhu, Senthil Purushwalkam, An Yan, Caiming Xiong, Ran Xu
ICCV 2025
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
Salesforce AI Research (Viraj Prabhu: core contributor)
ICCV 2025 Findings Workshop
We're Not Using Videos Effectively: An Updated Video Domain Adaptation Baseline
Simar Kareer, Vivek Vijaykumar, Harsh Maheshwari, Judy Hoffman, Prithvijit Chattopadhyay, Viraj Prabhu
TMLR 2024
Translating Labels to Solve Annotation Mismatches Across Object Detection Datasets
Yuan-Hong Liao, David Acuna, Rafid Mahmood, James Lucas, Viraj Prabhu, Sanja Fidler
ICLR 2024
AUGCAL: Sim-to-Real Adaptation by Improving Uncertainty Calibration on Augmented Synthetic Images
Prithvijit Chattopadhyay, Bharat Goyal, Bogi Ecsedi, Viraj Prabhu, Judy Hoffman
ICLR 2024
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay, Judy Hoffman
NeurIPS 2023
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Viraj Prabhu, David Acuna, Yuan-Hong Liao, Rafid Mahmood, Marc T. Law, Judy Hoffman, Sanja Fidler, James Lucas
TMLR 2023
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Vision Tasks
Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Adrien Bardes, Mark Ibrahim, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein
NeurIPS Datasets & Benchmarks 2023
FACTS: First Amplify Correlations and Then Slice to Discover Bias
Sriram Yenamandra, Pratik Ramesh, Viraj Prabhu, Judy Hoffman
ICCV 2023
ICON2: Reliably Benchmarking Inequity in Detection by Identifying and Controlling for Confounders
Sruthi Sudhakar, Viraj Prabhu, Olga Russakovsky, Judy Hoffman
CVPR Workshop 2023
Can domain adaptation make object recognition work for everyone?
Viraj Prabhu, Ramprasaath R. Selvaraju, Judy Hoffman, Nikhil Naik
CVPR Workshop 2022
Mitigating Bias in Visual Transformers via Targeted Alignment
Sruthi Sudhakar, Viraj Prabhu, Arvind Krishnakumar, Judy Hoffman
BMVC 2021
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
Viraj Prabhu*, Sriram Yenamandra*, Aaditya Singh, Judy Hoffman (* = equal contribution)
NeurIPS 2022
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Segmentation
Viraj Prabhu*, Shivam Khare*, Deeksha Kartik, Judy Hoffman (* = equal contribution)
ECCV Workshop 2022
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models
Arvind Krishnakumar, Viraj Prabhu, Sruthi Sudhakar, Judy Hoffman
BMVC 2021
Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
Viraj Prabhu, Shivam Khare, Deeksha Kartik, Judy Hoffman
ICCV 2021
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, Judy Hoffman
ICCV 2021
Open Set Medical Diagnosis
Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David Sontag, Xavier Amatriain
NeurIPS Workshop 2019
Few-shot Learning for Dermatological Disease Diagnosis
Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain
MLHC 2019 (spotlight)
Do Explanations make VQA Models more Predictable to a Human?
Arjun Chandrasekaran*, Viraj Prabhu*, Deshraj Yadav*, Prithvijit Chattopadhyay*, Devi Parikh (* = equal contribution)
EMNLP 2018
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
Aroma Mahendru*, Viraj Prabhu*, Akrit Mohapatra*, Dhruv Batra, Stefan Lee (* = equal contribution)
EMNLP 2017
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
Prithvijit Chattopadhyay*, Deshraj Yadav*, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh (* = equal contribution)
HCOMP 2017
Miscellaneous
Service & Recognition
- Reviewer: CVPR, NeurIPS, ICCV, ECCV, ICLR, TMLR, WACV, ACL (outstanding reviewer at NeurIPS 2021, CVPR 2021)
- Workshop organizer: EMACS (CVPR '25), L2ID (ECCV '22)
- Teaching Assistant: for Computer Vision (Spring '21), Deep Learning (Fall '19), Machine Learning (Fall 17')
Talks & Media
- Invited talks: Towards Reliable Computer Vision, at Caltech, AWS, UC Berkeley, CMU (2023-2024)
- Speaker: Human-Centered AI Tutorial (CVPR 2022), Industry Research Panel (CVPR 2025)
- News coverage: CoAct-1 (VentureBeat), BLIP-3 (VentureBeat), PACMAC & LANCE (GT News)
Projects & Software
- Agentic AI for Flow: Salesforce blog
- Fabrik: Neural network IDE (code)
- KeyframeCut: Adobe video segmentation tool
- Visual Dialog RL: PyTorch implementation