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Inspired by the recent neuroscience studies on the left-right asymmetry ofthe human brain in processing low and high spatial frequency information, thispaper introduces a dual skipping network which carries out coarse-to-fineobject categorization. Such a network has two branches to simultaneously dealwith both coarse and fine-grained classification tasks. Specifically, wepropose a layer-skipping mechanism that learns a gating network to predictwhich layers to skip in the testing stage. This layer-skipping mechanism endowsthe network with good flexibility and capability in practice. Evaluations areconducted on several widely used coarse-to-fine object categorizationbenchmarks, and promising results are achieved by our proposed network model.

Dual Skipping Networks