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Classification is a pivotal function for many computer vision tasks such asobject classification, detection, scene segmentation. Multinomial logisticregression with a single final layer of dense connections has become theubiquitous technique for CNN-based classification. While these classifiersproject a mapping between the input and a set of output category classes, theydo not typically yield a comprehensive description of the category. Inparticular, when a CNN based image classifier correctly identifies the image ofa Chimpanzee, its output does not clarify that Chimpanzee is a member ofPrimate, Mammal, Chordate families and a living thing. We propose a multilayerdense connectivity for concurrent prediction of category and its conceptualsuperclasses in hierarchical order by the same CNN. We experimentallydemonstrate that our proposed network can simultaneously predict both thecoarse superclasses and finer categories better than several existingalgorithms in multiple datasets.

Multilayer Dense Connections for Hierarchical Concept Classification