Labelling actions in videos
30 Jun 2020, 15:00 — Remote
Speaker:
Davide Moltisanti — Nanyang Technological University, Singapore
Davide Moltisanti — Nanyang Technological University, Singapore
Abstract:
In this talk I will start questioning the fact that semantic and temporal annotations are taken for granted in action recognition. I will show that this leads to some issues and will present two works to tackle such problems. I will then briefly talk about EPIC Kitchens, the largest Egocentric video dataset to which I contributed during my PhD, focusing mainly on how we annotated it. Finally, I will present my last work which proposes a weakly supervised method for action recognition using a novel type of temporal annotations, i.e. single timestamps roughly aligned with the actions.
In this talk I will start questioning the fact that semantic and temporal annotations are taken for granted in action recognition. I will show that this leads to some issues and will present two works to tackle such problems. I will then briefly talk about EPIC Kitchens, the largest Egocentric video dataset to which I contributed during my PhD, focusing mainly on how we annotated it. Finally, I will present my last work which proposes a weakly supervised method for action recognition using a novel type of temporal annotations, i.e. single timestamps roughly aligned with the actions.
Bio:
I took my PhD in Computer Science at the University of Bristol (UK) in November 2019. In March 2020 I joined the Nanyang Technological University of Singapore as a research fellow. My area of research is action recognition and video understanding. In particular my interest focuses on weakly supervised approaches and the link between labels and learning.
I took my PhD in Computer Science at the University of Bristol (UK) in November 2019. In March 2020 I joined the Nanyang Technological University of Singapore as a research fellow. My area of research is action recognition and video understanding. In particular my interest focuses on weakly supervised approaches and the link between labels and learning.