Digital Services        F A Q


Deep Representations, Adversarial Learning and Domain Adaptation for Some Computer Vision Problems

Dept. of Computer Science (July 6, 2018)

SEMINAR SERIES : Distinguished Lecture

MAJOR SPEAKER : Chellappa, Rama
LENGTH : 69 min.
ACCESS : Open to all
SUMMARY : Recent developments in deep representation-based methods for many computer vision problems have knocked down many research themes pursued over the last four decades. In this talk, I will discuss methods based on deep representations for designing robust computer vision systems with applications in unconstrained face and action verification and recognition, expression recognition, subject clustering and attribute extraction. The face and action recognition system being built at UMD is based on fusing multiple deep convolutional neural networks (DCNNs) trained using publicly available still and video face data sets and task appropriate loss functions. I will then discuss some new results on generative adversarial learning and domain adaptation for improving the robustness of computer vision systems.  [Go to the full record in the library's catalogue]

  ●  Persistent link:
  ●  XML Dublin Core code for metadata harvesting

Recommended for You

This video is presented here with the permission of the speakers. Any downloading, storage, reproduction, and redistribution are strictly prohibited without the prior permission of the respective speakers. Go to Full Disclaimer.

  For enquiries, please contact Digital and Multimedia Services Section

© 2009-2023 All rights reserved