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 various problems related to semantic understanding from large scale text and image data. I completed my undergraduate studies from IIT Guwahati in 2016, before working as a data scientist at Oracle Bangalore where I was part of team responsible for creating automated client-side cloud instance provisioning, upgrade and patching tools.
|May 2022: Selected as Highlighted reviewer at ICLR2022.|
|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 , Astuti Sharma, Manmohan Chandraker.
webpage / arxiv / pdf / code / reviews
Memory-based consistency losses to scale unsupervised domain adaptation to a large number of classes, including fine-grained datasets.
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.
Astuti Sharma, Tarun Kalluri , Manmohan Chandraker.
arxiv / code
Proposes multi sample contrastive loss using instance level similarities across source and target domains for robust feature alignment and knowledge transfer.
Tarun Kalluri , Deepak Pathak, Manmohan Chandraker, Du Tran
arxiv / pdf / project webpage / video / Code/ Colab
Fast, accurate and flow-free video frame interpolation technique using space-time convolutions, capable of single shot multi frame prediction.
Tarun Kalluri , Girish Varma, Manmohan Chandraker, CV Jawahar
pdf / poster / reviews / code
Addresses geographical disparities between semantic segmentation datasets with minimum labeling overhead from each domain.
Punyaslok Pattnaik, Shampa Raghunathan, Tarun Kalluri , Prabhakar Bhimalapuram, C. V. Jawahar, U. Deva Priyakumar
Jorurnal of Physical Chemistry A, 2020
pdf / chemRxiv
Neural network aided simulation of molecular forces making use of the data obtained using the quantummechanical density functional theory (DFT) on small systems.
Ashutosh Mishra*, Sudhir Kumar*, Tarun Kalluri* , Girish Varma, Anbumani Subramaian, Manmohan Chandraker, CV Jawahar
pdf / poster / reviews / dataset
Proposes optimum model and data resampling choices for resource constrained training of semantic segmentation networks.
Tarun Kalluri , Mansi Peer, Vivek Bohara, Daniel B. da Costa, Ugo S. Dias
IET Communications, 2018
Relaying protocols for energy harvesting wireless sensor networks under cooperative spectrum sharing.
In the past, I have been extremely fortunate to have been advised by the following mentors.
|||Domain adaptation for urban scene understanding, Augmented Reality and Self-Driving workshop, Qualcomm San Diego, June 2020 [slides][abstract]|
|||Cross Task Adaptation for semantic segmentation, Pixel Cafe, UCSD, May 2020|
|||Universal Semi-supervised Semantic Segmentation, Pixel Cafe, UCSD, Nov 2019 [slides]|