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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2022

Author dc:creator

• Smailis, Christos

Advisor dc:contributor.advisor

• Kakadiaris, Ioannis A.

Committee members dc:contributor.committeemember

• Paliouras, George
• Solorio, Thamar
• Huang, Stephen

• Deep learning
• LUPI
• Multimodal data fusion
• Knowledge distillation

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/14276

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/14276