Abstract
Inspired by the recent neuroscience studies on the leftright asymmetry of the human brain in processing low and
high spatial frequency information, this paper introduces a
dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping
mechanism that learns a gating network to predict which
layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Evaluations are conducted on several widely
used coarse-to-fine object categorization benchmarks, and
promising results are achieved by our proposed network
model