  A weakly-supervised semantic segmentation framework with a tied
deconvolutional neural network is presented. Each deconvolution layer in the
framework consists of unpooling and deconvolution operations. 'Unpooling'
upsamples the input feature map based on unpooling switches defined by
corresponding convolution layer's pooling operation. 'Deconvolution' convolves
the input unpooled features by using convolutional weights tied with the
corresponding convolution layer's convolution operation. The
unpooling-deconvolution combination helps to eliminate less discriminative
features in a feature extraction stage, since output features of the
deconvolution layer are reconstructed from the most discriminative unpooled
features instead of the raw one. This results in reduction of false positives
in a pixel-level inference stage. All the feature maps restored from the entire
deconvolution layers can constitute a rich discriminative feature set according
to different abstraction levels. Those features are stacked to be selectively
used for generating class-specific activation maps. Under the weak supervision
(image-level labels), the proposed framework shows promising results on lesion
segmentation in medical images (chest X-rays) and achieves state-of-the-art
performance on the PASCAL VOC segmentation dataset in the same experimental
condition.
