23 citations · 97 across the 40 of their papers we have counts for
7 papers · 1 filter
Learning a Neural Association Network for Self-supervised Multi-Object Tracking
Shuai Li, Michael Burke, Subramanian Ramamoorthy +1
This paper introduces a novel framework to learn data association for multi-object tracking in a self-supervised manner. Fully-supervised learning methods are known to achieve exce…
OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare
Chen Long-fei, Subramanian Ramamoorthy, Robert B Fisher
Vision-based motion estimation methods show promise in accurately and unobtrusively estimating human body motion for healthcare purposes. However, these methods are not specificall…
Monitoring Simulated Physical Weakness Using Detailed Behavioral Features and Personalized Modeling
Chen Long-fei, Muhammad Ahmed Raza, Craig Innes +2
Aging and chronic conditions affect older adults' daily lives, making the early detection of developing health issues crucial. Weakness, which is common across many conditions, can…
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du, Benedicte Legastelois, Bhargavi Ganesh +5
Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and othe…
Learning data association without data association: An EM approach to neural assignment prediction
Michael Burke, Subramanian Ramamoorthy
Data association is a fundamental component of effective multi-object tracking. Current approaches to data-association tend to frame this as an assignment problem relying on gating…
Lower Dimensional Kernels for Video Discriminators
Emmanuel Kahembwe, Subramanian Ramamoorthy
This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a…