Apoorva Beedu

I am a PhD candidate at Georgia Institute of Technology advised by Dr. Irfan Essa and Dr. Justin Romberg.

My research interests are primarily focused on understanding actions and object's states in videos, and have previously worked on 6D object pose estimation and human activity recognition.

During the summer of 2021, I was an intern at Facebook Reality Labs, collaborating with Chengde Wan and Robert Wang. In 2020, I undertook an internship at Microsoft Research with Dr. Amol Ambadekar. In the summers of '18 and '17, I interned at a startup called NodeIn, working with Dr. Suresh Kannan. Prior to joining Georgia Institute of Technology, I spent a year at Asteria Aerospace Limited as a software Engineer.

Email: abeedu3 [at] gatech [dot] edu

GitHub / Google Scholar / LinkedIn / CV

News

Jan 2024: Our paper On the Efficacy of Text-Based Input Modalities for Action Anticipation is on ArXiv.
Oct 2023: Presented our paper on Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition at PerDream@ICCV !
Dec 2022: Presented our papers Video based Object 6D Pose Estimation using Transformers and End-to-End Multimodal Representation Learning for Video Dialog at VTTA2022@NeuRIPS.
Oct 2022: Served as a reviewer for BMVC 2022 and NeuRIPS VTTA workshop.
Aug 2021: Served as a reviewer for BMVC 2021
May 2021: Starting summer internship at Facebook Reality Labs as a Research Intern - Interaction Tracking (PhD).
Sept 2020: Our paper on Masked Reconstruction Based Self-Supervision for Human Activity Recognition was accepted into ISWC 2020

Research

I'm interested in computer vision, machine learning, video analysis and multi-modal representations.

	On the Efficacy of Text-Based Input Modalities for Action Anticipation Apoorva Beedu, Karan Samel, Irfan Essa ArXiv preprint (Under review), 2024 arxiv Introduces contrastive learning for Action Anticipation and evaluates using text data as additional modalities.
	Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition Hyeongju Choi, Apoorva Beedu,Irfan Essa ICCV 2023 workshop on PerDream: PERception, Decision making and REAsoning through Multimodal foundational modeling, 2023 arxiv Introduces contrastive learning with hard negative sampling for Human Activity Recognition.
	Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition Hyeongju Choi, Apoorva Beedu, Harish Haresamudram, Irfan Essa ArXiv, 2022 arxiv We present a multi-modal framework that learns to effectively combine features from RGB Video and IMU sensors for Human Activity Recognition.
	End-to-End Multimodal Representation Learning for Video Dialog Huda Alamri, Anthony Bilic, Michael Hu, Apoorva Beedu, Irfan Essa NeurIPS 2022 workshop on Vision Transformers: Theory and Applications, 2022 arxiv We present a framework for video based dialog task.
	Video based Object 6D Pose Estimation using Transformers Apoorva Beedu, Huda Alamri, Irfan Essa NeurIPS 2022 workshop on Vision Transformers: Theory and Applications, 2022 arxiv We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos.
	Masked reconstruction based self-supervision for human activity recognition Harish Haresamudram,Apoorva Beedu, Varun Agrawal, Patrick L Grady, Irfan Essa, Judy Hoffman, Thomas Plötz Proceedings of the 2020 ACM International Symposium on Wearable Computers, 2020 arxiv We introduce masked reconstruction as a viable self-supervised pre-training objective for human activity recognition and explore its effectiveness in comparison to state-of-the-art unsupervised learning techniques.
	Location based payload imaging Apoorva, J and Mohan, Brinda and Beedu, Apoorva and Nayak, Mahendra M and Rao, Divya and Agrawal, VK 2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2015 arxiv We present a method to add the capability of imaging at any commanded latitude and longitude by including a provision to estimate the time required to reach the desired latitude and longitude using Location-Based Payload Imaging.