Hi!

I am a Ph.D. student in Computer Science at the University of Maryland, College Park advised by Prof. Abhinav Shrivastava. I am currently working on understanding videos through graphs and my plan is to apply these techniques to understand long videos.

Before starting my graduate studies, I worked in industry for 8 years in various research and engineering roles. I have primarily focused on perception and my most relevant experience in this domain has been in autonomous vehicles space. In my previous life, I worked as an Analyst with multiple banks. I received my bachelors from IIT Delhi.

News

• May 2025: Outstanding Reviewer at CVPR 2025
• May-Aug 2024: Applied Scientist Intern@Amazon Fashion: Worked on diffuson-based controllable indoor novel scene creation. Work is still under progress
• July 2024: 3 papers got accepted to ECCV 2024. Two of these works were as shared first-author
• May-Aug 2023: Applied Scientist Intern@Amazon Fashion
• Jan 2023: Organized the workshop Dealing with novelty in the Open Worlds (DNOW) @WACV 2023
• Oct-Dec 2022: Applied Scientist Intern(Co-op)@Amazon: Worked on Visual Geolocalization
• June 2022: Organized ObjClsDisc: In-the-Wild Object Discovery Challenge as part of the workshop Visual Perception and Learning in an Open World @CVPR 2022
• May-Aug 2022: Applied Scientist Intern@Amazon: Researched on edge-based object detection models and successfully deployed models for field trials
• Jan 2022: Organized the workshop Dealing with novelty in the Open Worlds (DNOW) @WACV 2022
• Sep 2021: PatchGame is accepted to NeurIPS'21!
• Jan 2021: I began my masters in CS@UMD!
• Sep 2020 - Jan 2021: Worked as a Research Intern @Swiggy
• Jun 2017 - Sep 2020: Built computer vision models and other cool stuff @Netradyne

Research Highlights

	TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations ICRA 2025 PAPER Preference feedback collected by human or VLM annotators is often noisy, presenting a significant challenge for preference-based reinforcement learning. To address this challenge, we propose TREND, a novel framework that integrates few-shot expert demonstrations with a tri-teaching strategy for effective noise mitigation. Shuaiyi Huang, Mara Levy, Anubhav Gupta, Daniel Ekpo, Ruijie Zheng, Abhinav Shrivastava
	Measuring Style Similarity in Diffusion Models ECCV 2024 PAPER PROJECT PAGE CODE Video Can we measure the style similarity between images? We propose a way to extract style from images. We call this Contrastive Style Descriptors (CSD). Using this model, we study the style replication in image generation models. Gowthami Somepalli*, Anubhav Gupta*, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava , Tom Goldstein.
	Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics ECCV 2024 PAPER PROJECT PAGE Video We show semantic capabilities in Implicit Neural Representations (INR) by proposing a novel framework that learns discriminative semantics in videos. Shishira R Maiya*, Anubhav Gupta* , Matt Gwilliam , Max Ehrlich , Abhinav Shrivastava
	LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation ECCV 2024 PAPER PROJECT PAGE CODE Video Modeling unseen 3D articulation states by interpolating across a learnable, view-invariant latent embedding space. Archana Swaminathan, Anubhav Gupta , Kamal Gupta, Shishira R Maiya, Vatsal Agarwal, Abhinav Shrivastava
	PatchGame - Learning to Signal Mid-level Patches in Referential Games NeurIPS 2021 PAPER Emergent communication via mid-level patches in a referential game played on a large-scale image dataset. Kamal Gupta, Gowthami Somepalli, Anubhav Gupta , Vinoj Jayasundara, Matthias Zwicker, Abhinav Shrivastava
	Mining points of interest via address embeddings: an unsupervised approach LocalRec '21: Proceedings of the 5th ACM SIGSPATIAL PAPER Unsupervised PoI mapping (polygon boundaries) using GPS, OpenStreetMaps and Address Information in highly dense environments Abhinav Ganesan, Anubhav Gupta, Jose Mathew
	Two Party Evaluation of the Open Visual World Under Review Defining a paradigm for unbiased evaluation in open worlds.