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I am a fourth-year PhD student in the CSE department at UCSD advised by Prof. Manmohan Chandrakar. My research interests span the areas of computer vision and machine learning. I am interested in learning representations of images and videos from limited labeled data that are useful and transferable across a wide range of domains. Specifically, I work on problems in domain adaptation, transfer learning, few shot learning and open world learning. Previously, I worked as a research assistant at Centre for Visual Information and Technology (CVIT), IIIT Hyderabad on semantic segmentation on Indian roads. I did my bachelors from IIT Guwahati in 2016. |
Email / CV / Google Scholar / GitHub / LinkedIn / Twitter
June 2023: Started summer internship at Google Research in Mountain View campus! |
May 2023: We are conducting a workshop on geographical robustness in computer vision at ICCV 2023, please see the website for details! |
Mar 2023: GeoNet accepted to CVPR 2023! |
Jan 2023: FLAVR got selected as the Best Paper Finalist WACV 2023! |
July 2022: MemSAC accepted to ECCV 2022. |
2022: Selected as Highlighted reviewer at ICLR2022 and Neurips 2022. |
June 2021: Started internship at FAIR, MPK to work on open-world learning. |
May 2021: One paper on domain adaptation accepted at CVPR 2021! |
Sep 2020: Recipient of IPE PhD fellowship for the year 2020-21. |
Aug 2020: Our paper on training neural network for molecular force predictions is accepted to the Journal of Physical Chemistry. |
June 2020: Joined Facebook AI as a summer intern with the multimodal learning group. |
Dec 2019: One paper on datasets for resource constrained semantic segmentation accepted to NCVPRIPG 2019. |
Sep 2019: Moved to sunny San Diego! Starting as a PhD student at CSE department in UCSD from Fall 2019. I will be working with the Visual Computing Group. |
Jul 2019: Our paper on Universal Semantic Segmentation is accepted to ICCV 2019. |
Tarun Kalluri , Wangdong Xu, Manmohan Chandraker. CVPR, 2023 webpage / arxiv / pdf / code / reviews / poster New large-scale dataset to study generalization properties of vision models across disparate geographies. |
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Tarun Kalluri , Weiyao Wang, Heng Wang, Manmohan Chandraker, Lorenzo Torresani, Du Tran In Submission, 2023 project page / arxiv / pdf / code |
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Tarun Kalluri , Astuti Sharma, Manmohan Chandraker. ECCV, 2022 webpage / arxiv / pdf / code / reviews / poster Memory-based consistency losses to scale unsupervised domain adaptation to a large number of classes, including fine-grained datasets. |
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Selected as finalist for Best Paper Award WACV, 2023 arxiv / pdf / project webpage / video / Code/ reviews / Colab Fast, accurate and flow-free video frame interpolation technique using space-time convolutions, capable of single shot multi frame prediction. |
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Tarun Kalluri , Manmohan Chandraker. L3D Workshop, CVPR, 2022 pdf / poster Domain adaptation across datasets with disjoint labels using a deep-clustering based approach and an intermediate bridge domain. |
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Astuti Sharma, Tarun Kalluri , Manmohan Chandraker. CVPR, 2021 arxiv / code Proposes multi sample contrastive loss using instance level similarities across source and target domains for robust feature alignment and knowledge transfer. |
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Tarun Kalluri , Girish Varma, Manmohan Chandraker, CV Jawahar ICCV, 2019 pdf / poster / reviews / code Addresses geographical disparities between semantic segmentation datasets with minimum labeling overhead from each domain. |
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Ashutosh Mishra*, Sudhir Kumar*, Tarun Kalluri* , Girish Varma, Anbumani Subramaian, Manmohan Chandraker, CV Jawahar NCVPRIPG, 2019 pdf / poster / reviews / dataset Proposes optimum model and data resampling choices for resource constrained training of semantic segmentation networks. |
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