Divya Kothandaraman

I am a Computer Science PhD candidate at the University of Maryland College Park, working with Prof. Dinesh Manocha and Prof. Ming Lin at the GAMMA Lab. My primary research focus is computer vision and deep learning. Previously, I was an undergraduate (five year dual degree) at the Indian Institute of Technology Madras, where I obtained a bachelors degree in Electrical Engineering, and masters degree in Data Sciences.

My current research is in generative AI, with a special focus on image personalization and view synthesis. In the past, I have worked on video action recognition and domain adaptation.

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I am on the industry job market, looking for full-time Research Scientist roles. Please reach out if you feel I could be a good fit to your team.
Latest News
  • (Mar 2024) Gave a talk at UCL! Slides here.
  • (Nov 2023) AerialBooth is on ArXiv!
  • (Sep 2023) Aerial Diffusion has been accepted to Siggraph Asia 2023.
  • (May 2023) Interning at Google DeepMind.
  • (Jan 2023) Differentiable FAR has been accepted to ICRA 2023.
  • (Oct 2022) Two papers have been accepted to WACV 2023.
  • (July 2022) FAR: Fourier Aerial Video Recognition has been accepted to ECCV 2022.
Research Highlights
Aerial_Booth HawkI: Homography and Mutual Information Guidance for 3D-free Single Image to Aerial View
Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha
arXiv / GitHub

Mutual information and inverse perspective mapping guidance for text-controlled aerial view synthesis from a single input image using diffusion models.

Aerial_Diffusion Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models
Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha
Siggraph Asia 2023 (Conference Proceedings, Technical Communications)
arXiv / GitHub

A text-guided image to image diffusion model to generate aerial views from a single ground-view image.

DifFAR Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition
Divya Kothandaraman, Ming Lin, Dinesh Manocha
ICRA 2023
arXiv / GitHub

A differentiable feature disentanglement method to learn "static salient" and "dynamic salient" regions for aerial video action recognition.

SALAD SALAD: Source-free Active Label Agnostic Domain Adaptation
Divya Kothandaraman, Sumit Shekhar, Abhilasha Sancheti, Manoj Ghuhan, Tripti Shukla, Dinesh Manocha
WACV 2023
arXiv / GitHub

A generic source-free active domain adaptation method that can handle shifts in output label space.

FAR FAR: Fourier Aerial Video Recognition
Divya Kothandaraman, Tianrui Guan, Xijun Wang, Sean Hu, Ming Lin, Dinesh Manocha
ECCV 2022
Project Page / arXiv / GitHub

An efficient aerial video action recognition method, with novel frequency domain techniques, vis-a-vis, Fourier object disentanglement and Fourier attention.

GANAV GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments
Tianrui Guan Divya Kothandaraman, Rohan Chandra Dinesh Manocha
IROS 2022 and RSS 2022
Project Page / arXiv / bibtex

An attention-based segmentation method for identifying safe and navigable regions in off-road terrains.

SS-SFDA SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments
Divya Kothandaraman, Rohan Chandra Dinesh Manocha
ICCV Workshops 2021
Project Page / arXiv / YouTube / GitHub / bibtex

A self-supervised learning approach for source free unsupervised road segmentation in adverse weather environments and low light conditions.

BOMUDA BoMuDA: Boundless Multi-Source Domain Adaptive Segmentation in Unconstrained Environments
Divya Kothandaraman, Rohan Chandra Dinesh Manocha
ICCV Workshops 2021
Project Page / arXiv / YouTube / GitHub / bibtex

A multi-source boundless unsupervised domain adaptation algorithm for semantic segmentation in unstructured environments.

WACV21IITM Domain Adaptive Knowledge Distillation for Driving Scene Semantic Segmentation
Divya Kothandaraman, Athira Nambiar Anurag Mittal
WACV Workshops 2021
Paper / YouTube / GitHub / bibtex

An approach for domain adaptive semantic segmentation in models with limited memory.

ECCVW20 Deep Atrous Guided Filter for Image Restoration in Under Display Cameras
Varun Sundar , Sumanth Hegde*, Divya Kothandaraman , Kaushik Mitra
ECCV Workshops, 2020
ArXiv / YouTube / Project Page / bibtex

Guided Filters when incorporated in a deep network can efficiently recover severely degraded, mega-pixel resolution images.

Website template borrowed from Jon Barron.