[Defense] Action Labeling in Images and Video
Tuesday, April 19, 2022
9:00 am - 10:30 am
will defend his dissertation
Action Labeling in Images and Video
Deep learning models that attempt to categorize visual content can benefit from being trained with additional information that may be, or may not be, available during deployment. To this end, this dissertation designed, developed, and evaluated methods inspired by the “Learning Using Privileged Information” framework, multimodal data fusion, and knowledge distillation to improve deep learning models’ performance. These methods are assessed for the problems of: (i) recognizing carrying actions in “visible spectrum” and “near-infrared” images, as well as (ii) detecting questionable online video content. The experimental results demonstrated the effectiveness of the methods in four new datasets introduced within the context of this work to address the challenges of the problems mentioned above.
9:00AM - 10:30AM CT
Virtual via MS Teams
Dr. Ioannis Kakadiaris, dissertation advisor
Faculty, students and the general public are invited.