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Distinguished lecture (Hong Kong Baptist University. Dept. of Computer Science)

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

DEPARTMENT:

Department of Computer Science

CONYTRIBUTOR / SPEAKER:

CHELLAPPA, Rama

RELEASE DATE:

2018

LENGTH:

69 Minutes

VIDEO TYPE:

Scholarly Talks

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.

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

部門:

計算機科學系

講者:

CHELLAPPA, Rama

發佈日期:

2018

影片長度:

69 分鐘

影片類型:

學術論壇

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

部门:

计算机科学系

讲者:

CHELLAPPA, Rama

发布日期:

2018

影片长度:

69 分钟

影片类型:

学术论坛

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