Multilayer Dense Connections for Hierarchical Concept Classification

Toufiq Parag, Hongcheng Wang

Keywords: Deep Learning: Applications, Methodology, and Theory, Recognition: Detection, Categorization, Retrieval and Matching, Scene Understanding, Visual Reasoning

Abstract: Classification is a pivotal function for many computer vision tasks such as image recognition, object detection, scene segmentation. Multinomial logistic regression with a single final layer of dense connections has become the ubiquitous technique for CNN-based classification. While these classifiers project a mapping between the input and a set of output category classes, they do not typically yield a comprehensive description of the category. In particular, when a CNN based image classifier correctly identifies the image of a Chimpanzee, its output does not clarify that Chimpanzee is a member of Primate, Mammal, Chordate families and a living thing. We propose a multilayer dense connectivity for a CNN to simultaneously predict the category \emph{and} its conceptual superclasses in hierarchical order. We experimentally demonstrate that our proposed dense connections, in conjunction with popular convolutional feature layers, can learn to predict the conceptual classes with minimal increase in network size while maintaining the categorical classification accuracy.

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