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Feedforward semantic segmentation with zoom-out features

2019-12-25 | |  67 |   39 |   0

Abstract

We introduce a purely feed-forward architecture for semantic segmentation. We map small image elements (su-perpixels) to rich feature representations extracted from a sequence of nested regions of increasing extent. These re-gions are obtained by ”zooming out” from the superpixelall the way to scene-level resolution. This approach exploitsstatistical structure in the image and in the label space with-out setting up explicit structured prediction mechanisms,and thus avoids complex and expensive inference. Insteadsuperpixels are classified by a feedforward multilayer net-work. Our architecture achieves 69.6% average accuracyon the PASCAL VOC 2012 test set.

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