representation learning

Transformers from Compressed Representations

Juan Carlos L. Alcazar, Research Scientist, Electrical and Computer Engineering

Oct 11, 12:00 - 13:00

B9 R2325

transformers representation learning TEMPEST visual computing AI machine learning

This talk explores how transformers can effectively be trained on compressed byte streams in order to gain computational and memory efficiencies.

Drishya Karki

Research Engineer

Computer Vision Temporal Activity Localization Multi-Modal Learning representation learning Graph Learning

Research Engineer at the KAUST–FIFA Research Institute, working on multimodal sports understanding using video, player positional tracking, and skeletal-joint tracking data. My research focuses primarily on group activity recognition and action spotting.

Habib Slim

Ph.D. Student, Computer Science

Computer Vision Continual Learning Deep learning representation learning

Habib Slim is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Education: M.Sc Data Science, Grenoble Institute of Technology, France, 2021 M.Eng Applied Mathematics and Computer Science, Grenoble Institute of Technology, France, 2021