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Mining Deep And-Or Object Structures via Cost-Sensitive Question-Answer-Based Active Annotations
[article]
2019
arXiv
pre-print
This paper presents a cost-sensitive active Question-Answering (QA) framework for learning a nine-layer And-Or graph (AOG) from web images. The AOG explicitly represents object categories, poses/viewpoints, parts, and detailed structures within the parts in a compositional hierarchy. The QA framework is designed to minimize an overall risk, which trades off the loss and query costs. The loss is defined for nodes in all layers of the AOG, including the generative loss (measuring the likelihood
arXiv:1708.03911v3
fatcat:enuowzz2ojbcdfy6gqhffhnmqi