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Images of scenes have various objects as well as abundant attributes, anddiverse levels of visual categorization are possible. A natural image could beassigned with fine-grained labels that describe major components,coarse-grained labels that depict high level abstraction or a set of labelsthat reveal attributes. Such categorization at different concept layers can bemodeled with label graphs encoding label information. In this paper, we exploitthis rich information with a state-of-art deep learning framework, and proposea generic structured model that leverages diverse label relations to improveimage classification performance. Our approach employs a novel stacked labelprediction neural network, capturing both inter-level and intra-level labelsemantics. We evaluate our method on benchmark image datasets, and empiricalresults illustrate the efficacy of our model.

Learning Structured Inference Neural Networks with Label Relations