activity
20172025
most citedFAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection

2 citations · 3 across the 5 of their papers we have counts for

collaborators

12 papers

cs.CV2025

Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset

Vasu Agrawal, Akinniyi Akinyemi, Kathryn Alvero +81

Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals. To develop socially intelligent…

cs.CV2022★ 2 cited

FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection

Jing Yang, Jie Shen, Yiming Lin +2

Due to its importance in facial behaviour analysis, facial action unit (AU) detection has attracted increasing attention from the research community. Leveraging the online knowledg…

cs.PL2021★ 1 cited

Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications

Craig Innes, Yordan Hristov, Georgios Kamaras +1

This paper presents an extension to the probabilistic programming language ProbRobScene, allowing users to automatically synthesize uniform experiment designs directly from environ…

cs.AI2020

From Demonstrations to Task-Space Specifications: Using Causal Analysis to Extract Rule Parameterization from Demonstrations

Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy

Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work, we show that i…

cs.RO2020

Learning from Demonstration with Weakly Supervised Disentanglement

Yordan Hristov, Subramanian Ramamoorthy

Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teaching robots, is difficul…

cs.RO2019

Disentangled Relational Representations for Explaining and Learning from Demonstration

Yordan Hristov, Daniel Angelov, Michael Burke +2

Learning from demonstration is an effective method for human users to instruct desired robot behaviour. However, for most non-trivial tasks of practical interest, efficient learnin…