Deep learning tools for pose estimation in the laboratory
PLEASE NOTE THE VENUE HAS CHANGED TO THE SMALL LECTURE THEATRE AT THE SHERRINGTON BUILDING.
Please contact Tai-Ying Lee (tai-ying.lee@dpag.ox.ac.uk) if you would like to arrange an individual meeting with the speaker
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. I will discuss an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). In this work, we demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors.
Date:
13 May 2019, 16:00 (Monday, 3rd week, Trinity 2019)
Venue:
Sherrington Building, off Parks Road OX1 3PT
Venue Details:
Small lecture theatre
Speaker:
Dr Mackenzie Mathis (Harvard University)
Organiser:
Cortex Club (University of Oxford)
Organiser contact email address:
tai-ying.lee@dpag.ox.ac.uk
Host:
Tai-Ying Lee
Part of:
Cortex Club - Oxford Neuroscience Society
Booking required?:
Not required
Audience:
Public
Editor:
Tai-Ying Lee
