Tarun Kalluri

Tarun Kalluri

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.

GeoNet: Benchmarking Unsupervised Adaptation Across Geographies
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.

Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision
Tarun Kalluri , Weiyao Wang, Heng Wang, Manmohan Chandraker, Lorenzo Torresani, Du Tran
In Submission, 2023
project page / arxiv / pdf / code
MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation
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.

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

Selected as finalist for Best Paper Award
(Top 12 out of 641 papers, Top 2%).

Tarun Kalluri , Deepak Pathak, Manmohan Chandraker, Du Tran
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.

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels
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.

Instance Level Affinity Based Transfer for Unsupervised Domain Adaptation
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.

Universal Semi-Supervised Semantic Segmentation
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.

Semantic Segmentation Datasets for Resource Constrained Training
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.

Template modified from Jon Barron and Micheal Hahn