Paritosh Parmar

Paritosh
Parmar

I am a Computer Vision Scientist at A*STAR, Singapore. Prior to that, I was a Postdoctoral Research Fellow at the University of British Columbia, Vancouver. University of Nevada, Las Vegas was a great place to do my PhD. My research interests primarily lie in video understanding/video analysis (action recognition, action quality assessment, sports performance analysis, video captioning/description/QA, action anticipation, etc.).

I am always open to collaboration, so feel free to contact me if you are looking to collaborate.

Research

	CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes Paritosh Parmar, Eric Peh, Ruirui Chen, Ting En Lam, Yuhan Chen, Elston Tan, Basura Fernando NeurIPS, 2024 project page \| arxiv \| dataset \| code TL;DR: We capitalize on cartoons and their properties to create a novel, challenging, focused dataset for causal video question answering; study the performance of various models; show improvements on real-world datasets.
	Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment Lauren Okamoto, Paritosh Parmar CVPR 2024 CVSports, 2024 (🏆 Best Paper Award; Oral Presentation; 3/3 Strong Accepts; Selected for CVPR 2024 Demos 👩‍🔬) arxiv \| dataset \| code \| demo TL;DR: We propose a Hierarchical Neurosymbolic approach for comprehensive and explainable action analysis.
	Learning to Visually Connect Actions and their Effects Paritosh Parmar, Eric Peh, Basura Fernando WACV, 2025 (2 Reviewer Nominations for Best Paper Candidate; 2 Strong Accepts) arxiv \| dataset (coming soon) \| code (coming soon) TL;DR: Video understanding has made tremendous progress. However, it has not been explored if video understanding can connect actions and their effects. In this pilot study, we introduce the concept of connecting actions and their effects in video understanding literature.
	Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment Paritosh Parmar, Amol Gharat, Helge Rhodin ECCV, 2022 arxiv \| dataset \| code TL;DR: We proposed three self-supervised approaches to learn pose-sensitive representations for workout form assessment: Pose Contrastive learning; Disentangling Erroneous Motion from Regular Motion; Disentangling Pose and Appearance.
	Win-Fail Action Recognition Paritosh Parmar, Brendan Morris WACV Workshops, 2022 arxiv \| dataset TL;DR: Trying to shift focus from just recognizing patterns to gaining truer understanding of videos by teaching CNNs to differentiate between successful and failed attempts at various tasks.
	Piano Skills Assessment Paritosh Parmar, Jaiden Reddy, Brendan Morris MMSP, 2021 arxiv \| dataset \| 📺 media coverage TL;DR: Automated, data-driven approach to assessing piano skills.
	HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN Paritosh Parmar, Brendan Morris Signals, 2(3), 2021 arxiv \| code TL;DR: Approximating a 3DCNN with a 2DCNN, and the benefits of doing so.
	What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment Paritosh Parmar, Brendan Morris CVPR, 2019 (Oral Presentation) arxiv \| dataset \| code \| 📲 app TL;DR: Learning to mimic commentators can help better mimic judges.
	Action Quality Assessment Across Multiple Actions Paritosh Parmar, Brendan Morris WACV, 2019 arxiv \| dataset \| code TL;DR: It is better to simultaneously learn to mimic human judges from multiple sports.
	Learning to Score Olympic Events Paritosh Parmar, Brendan Morris CVPR Workshops, 2017 (Oral Presentation; 🏆 Best Paper Award-3rd Place) arxiv \| dataset \| code \| 📚 external coverage TL;DR: Mimic human judges.
	Measuring the Quality of Exercises Paritosh Parmar, Brendan Morris EMBC, 2016 arxiv TL;DR: To facilitate the collection of physiotherapy assessment dataset without a physiotherapist, we treat AQA as a classification problem, instead of a regression problem. Further, we conduct a feasibility study on the same.

Professional Service

Conferences	CVPR • ECCV • WACV • ICCV • NeurIPS • AAAI • CHI • IROS • 3DV • Conference on Robots and Vision (CRV) • International Conference on Intelligent Transportation Systems (ITSC) • Intelligent Vehicles Symposium (IV) • Midwest Symposium on Circuits and Systems (MWSCAS)
Journals	TPAMI • IJCV • Machine Vision and Applications (MVA) • Transactions on Circuits and Systems for Video Technology (TCSVT) • Journal of Electronic Imaging (JEI) • Robotics and Automation Letters (RA-L) • Sensors • Applied Sciences • Sports Engineering • Journal of New Music Research

Team

	Lauren Okamoto (Student, Princeton CS Undergrad), Feb'23 - Jul'24 Ting En Lam (Intern, NTU CS Undergrad), Jan'23 - Jul'23 Yuhan Chen (Intern, NUS EE Undergrad), Jan'23 - Jun'23 Elston Tan (Intern, SP AI Undergrad), Dec'22 - Dec'23 Eric Peh (Research Engineer), Aug'22 - Ongoing
	Deyu (Jerry) Liu (CS Undergrad), Fall'21, Spring'22 Wei Zhi (Matthew) Tang (CS Undergrad), Summer'21

Teaching

Spring'19: Signals & Systems I - Discussion • Circuits I - Discussion
Fall'18: Digital Signal Processing - Lab • Electronics I - Lab
Spring'18: Circuits II - Lab
Fall'17: Digital Signal Processing - Lab
Spring'17: Signals & Systems I - Discussion
Spring'16: Computer Logic Design II - Discussion
Fall'15: Computer Logic Design I - Lab

based on a template by Jonathan Barron