We are interested in learning representations (features) that are discriminative for semantic image understanding tasks such as object classification, detection, and segmentation in images. A common approach to obtain such features is to use supervised learning. However, this requires manual annotation of images, which is costly, time-consuming, and prone to errors. In contrast, unsupervised or self-supervised feature learning methods exploiting unlabeled data can be much more scalable and flexible. I will present some of our efforts in this direction.
ACM Faculty Talk Series
Semi-supervised Learning for Visual Recognition
ACM Faculty Talk Series - 2
Semi-supervised Learning for Visual Recognition
Dr. Hamed Pirsiavash, Assistant Professor, CSEE
1 pm - 2 pm Friday, February 23, 2018, ITE 325, UMBC
Regards,
ACM Committee
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