Self-supervised learning for visual recognition

Hamed Pirsiavash
University of Maryland Baltimore County

We are interested in learning visual 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 recent efforts in this direction.

Presentation (PDF File)

Back to Workshop IV: Deep Geometric Learning of Big Data and Applications