Visual Representations and Models: From Latent SVM to Deep Learning
Tid: Ti 2016-09-27 kl 10.00 Plats: Kollegiesalen, Brinellvägen 8, plan 4, KTH Campus, Stockholm Abstract Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. This thesis, in its general form, proposes different techniques within the frameworks of two learning systems for representation and modeling. Namely, latent support vector machines (latent SVMs) and deep learning. First, we propose various approaches to group the positive samples into clusters of visually similar instances. Given a fixed representation, the sampled space of the positive distribution is usually structured. The proposed clustering techniques include a novel similarity measure based on exemplar learning, an approach for using additional annotation, and augmenting latent…
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