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We propose to model complex visual scenes using a non-parametric Bayesianmodel learned from weakly labelled images abundant on media sharing sites suchas Flickr. Given weak image-level annotations of objects and attributes withoutlocations or associations between them, our model aims to learn the appearanceof object and attribute classes as well as their association on each objectinstance. Once learned, given an image, our model can be deployed to tackle anumber of vision problems in a joint and coherent manner, including recognisingobjects in the scene (automatic object annotation), describing objects usingtheir attributes (attribute prediction and association), and localising anddelineating the objects (object detection and semantic segmentation). This isachieved by developing a novel Weakly Supervised Markov Random Field StackedIndian Buffet Process (WS-MRF-SIBP) that models objects and attributes aslatent factors and explicitly captures their correlations within and acrosssuperpixels. Extensive experiments on benchmark datasets demonstrate that ourweakly supervised model significantly outperforms weakly supervisedalternatives and is often comparable with existing strongly supervised modelson a variety of tasks including semantic segmentation, automatic imageannotation and retrieval based on object-attribute associations.

Weakly Supervised Image Annotation and Segmentation with Objects and Attributes