
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 virajp@gatech.edu.
News
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[May '23] Preprints on Language-guided Counterfactual Image Generation and adapting object detectors out on arXiv!.
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[Jan '23] Gave an invited talk at Google Research Zurich on Reliable Computer Vision
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[Sep '22] Adapting Self-supervised Vision Transformers was accepted at NeurIPS 2022!
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[Jun '22] Gave a tutorial on Human-Centered AI for Computer Vision at CVPR 2022.
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[May '22] Co-organizing the Learning from Limited and Imperfect Data workshop at ECCV 2022.
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[Apr '22] Can domain adaptation make object recognition work for everyone? was accepted to L3D-IVU at CVPR '22.
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[Oct '21] Papers on bias discovery and mitigation accepted at BMVC 2021!
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[Oct '21] Recognized as an outstanding reviewer at NeurIPS 2021.
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[Jul '21] Two papers on Unsupervised DA and Active DA accepted to ICCV 2021!
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[Jul '21] Preprint on source-free domain adaptative semantic segmentation is out on arXiv.
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[Jun '21] Recognized as an outstanding reviewer for CVPR 2021.
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[Jan '21] Head TA for Intro to Computer Vision, Spring 2021.
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[Dec '20] Preprint on Selective Entropy Optimization via Committee Consistency for Unsupervised DA is out on arXiv.
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[Oct '20] Preprint on Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings is out on arXiv.
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[Oct '19] Open Set Medical Diagnosis was accepted to the ML4H workshop at NeurIPS '19.
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[Sep '19] Fabrik: An Online Collaborative Neural Network Editor will appear at the Workshop on AI Systems, SOSP '19.
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[May '19] Completed my Master's degree!
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[May '19] Few-Shot Learning for Dermatological Disease Diagnosis was accepted as a spotlight to MLHC 2019.
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[Mar '19] Awarded the Georgia Tech College of Computing's MS Research award (1 student annually).
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[Aug '18] Do Explanations make VQA Models more Predictable to a Human? was accepted to EMNLP 2018.
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Reviewer for ICLR, CVPR, ECCV, NeurIPS (adjudged top-30% of reviewers) 2018.
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[Apr '18] Just released our PyTorch implementation of Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning on GitHub.
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[Jul '17] Presented a demo on Visual Chatbots at CVPR 2017.
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[Jul '17] Presented It Takes Two to Tango: Towards Theory of AI's Mind at the Chalearn Looking at People Workshop at CVPR 2017.
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[Jun '17] The Promise of Premise: Harnessing Question Premises in Visual Question Answering was accepted at EMNLP 2017.
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[Jun '17] Evaluating Visual Conversational Agents via Cooperative Human-AI Games was accepted at HCOMP 2017.
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Publications
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LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual ImagesPaper Project Page |
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Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and ReweightingPaper |
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Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking ConsistencyPaper News |
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Can domain adaptation make object recognition work for everyone?Paper |
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AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic SegmentationComputer Vision in the Wild workshop, ECCV 2022 (spotlight) Paper Video |
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UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition ModelsPaper Code |
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Mitigating Bias in Visual Transformers via Targeted AlignmentPaper |
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Selective Entropy Optimization via Committee Consistency for Unsupervised Domain AdaptationPaper Project Page Code Video Slides Poster |
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Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsPaper Project Page Code Video Slides Poster |
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Open Set Medical DiagnosisPaper |
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Few-shot Learning for Dermatological Disease DiagnosisMLHC 2019 (spotlight), ML4H workshop at NeurIPS 2018 Paper |
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Do Explanations make VQA Models more Predictable to a Human?EMNLP 2018, Chalearn Looking at People Workshop, CVPR 2017 Paper |
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The Promise of Premise: Harnessing Question Premises in Visual Question AnsweringPaper Code Dataset |
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Evaluating Visual Conversational Agents via Cooperative Human-AI GamesPaper Code |
Projects
Fabrik: An Online Collaborative Neural Network EditorReport Code |
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PyTorch implementation of Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode |
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Adobe Captivate Prime |
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Automated camera calibration and boresighting |
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KeyframeCutDemo Blog |
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