We address the problem of localisation of objects as bounding boxes in images
and videos 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. In this paper, a novel
framework based on Bayesian joint topic modelling is proposed, which differs
significantly from the existing ones in that: (1) All foreground object classes
are modelled jointly in a single generative model that encodes multiple object
co-existence so that "explaining away" inference can resolve ambiguity and lead
to better learning and localisation. (2) Image backgrounds are shared across
classes to better learn varying surroundings and "push out" objects of
interest. (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. Moreover, the Bayesian formulation enables the
exploitation of various types of prior knowledge to compensate for the limited
supervision offered by weakly labelled data, as well as Bayesian domain
adaptation for transfer learning. Extensive experiments on the PASCAL VOC,
ImageNet and YouTube-Object videos datasets demonstrate the effectiveness of
our Bayesian joint model for weakly supervised object localisation.

本文提出了一种基于贝叶斯联合主题建模的新框架，通过学习较弱标签的数据，将前景对象类建模到单个生成模型中，并利用多类型的先验知识来提高学习和定位效果，因此能够更好地解决对象定位问题。

弱标注图像中物体定位的贝叶斯联合建模

Bayesian Joint Modelling for Object Localisation in Weakly Labelled  Images

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.

本研究基于贝叶斯共同主题建模，提出解决弱标签图像目标定位问题的新框架，同时考虑目标与背景，可利用先验知识补偿有限监督，在混合弱标注和未标注数据的情况下学习，结果显示在 VOC 数据集上优于现有方法。