6 citations · 6 across the 3 of their papers we have counts for
11 papers · 1 filter
MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching
Hao Yin, Paritosh Parmar, Lijun Gu +6
Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics such as muscle mechanics and…
Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition
Paritosh Parmar, Landy Lan, Hong Yang +2
Can computer vision help make classrooms safer? In this pilot study, we investigate privacy-aware and computationally efficient classroom incident recognition from CCTV-style obser…
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Paritosh Parmar, Eric Peh, Basura Fernando
Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipelines that entangle video understanding,…
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Paritosh Parmar, Eric Peh, Ruirui Chen +4
Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address this gap, we capitalize on the unique…
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
Lauren Okamoto, Paritosh Parmar
Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models…
Learning to Visually Connect Actions and their Effects
Paritosh Parmar, Eric Peh, Basura Fernando
We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding. CATE can have applications in areas like task planning and learning f…