2 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition
Alexander Vedernikov
Engagement recognition datasets are typically subject-indexed and often contain noisy, subjective supervision, making post-hoc dataset revision a practical problem. Existing noisy-…
PriorNet: Prior-Guided Engagement Estimation from Face Video
Alexander Vedernikov
Engagement estimation from face video remains challenging because facial evidence is often incomplete, labeled data are limited, and engagement annotations are subjective. We prese…
Vision Large Language Models Are Good Noise Handlers in Engagement Analysis
Alexander Vedernikov, Puneet Kumar, Haoyu Chen +2
Engagement recognition in video datasets, unlike traditional image classification tasks, is particularly challenged by subjective labels and noise limiting model performance. To ov…
TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals
Alexander Vedernikov, Puneet Kumar, Haoyu Chen +2
Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need lar…
Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features
Alexander Vedernikov, Zhaodong Sun, Virpi-Liisa Kykyri +3
Engagement measurement finds application in healthcare, education, services. The use of physiological and behavioral features is viable, but the impracticality of traditional physi…