Adobe Inc.
WINDOWED CONTEXTUAL POOLING FOR OBJECT DETECTION NEURAL NETWORKS

Abstract:

Techniques are disclosed for neural network based windowed contextual pooling. A methodology implementing the techniques according to an embodiment includes segmenting input feature channels into first and second groups of feature channels. The method also includes applying a first windowed pooling process to the first group of feature channels to generate a first group of pooled feature channels and applying a second windowed pooling process to the second group of feature channels to generate a second group of pooled feature channels. The method further includes performing a weighted merging of the first group of pooled feature channels and the second group of pooled feature channels to generate merged pooled feature channels. The method further includes concatenating the merged pooled feature channels with the input feature channels to generate concatenated feature channels and applying a two-dimensional convolutional neural network to the concatenated feature channels to generate contextually pooled output feature channels.