Title: Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation,
Abstract: We address the problem of localisation of objects as bounding boxes in images
with weak labels. This weakly supervised object localisation problem has been
tackled in the past using discriminative models where each object class is
localised independently from other classes. We propose a novel framework based
on Bayesian joint topic modelling. Our framework has three distinctive
advantages over previous works: (1) All object classes and image backgrounds
are modelled jointly together in a single generative model so that "explaining
away" inference can resolve ambiguity and lead to better learning and
localisation. (2) The Bayesian formulation of the model enables easy
integration of prior knowledge about object appearance to compensate for
limited supervision. (3) Our model can be learned with a mixture of weakly
labelled and unlabelled data, allowing the large volume of unlabelled images on
the Internet to be exploited for learning. Extensive experiments on the
challenging VOC dataset demonstrate that our approach outperforms the
state-of-the-art competitors.