弱监督目标定位的贝叶斯联合主题模型
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
摘要：
我们解决的问题，在图像中的边界框弱标签的对象的本地化。这种弱监督的对象定位问题在过去已经使用判别模型来解决，其中每个对象类独立于其他类进行定位。我们提出了一个新的框架的基础上贝叶斯联合主题建模。我们的框架有三个显着的优势，比以前的作品：（1）所有的对象类和图像背景一起建模在一个单一的生成模型，使“解释”的推理可以解决歧义，并导致更好的学习和本地化。(2)该模型的贝叶斯公式可以很容易地整合有关对象外观的先验知识，以弥补有限的监督。(3)我们的模型可以通过弱标记和未标记数据的混合来学习，从而可以利用互联网上大量的未标记图像进行学习。在具有挑战性的VOC数据集上进行的大量实验表明，我们的方法优于最先进的竞争对手。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
