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]
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