Title: Dense Transformer Networks,
Abstract: The key idea of current deep learning methods for dense prediction is to
apply a model on a regular patch centered on each pixel to make pixel-wise
predictions. These methods are limited in the sense that the patches are
determined by network architecture instead of learned from data. In this work,
we propose the dense transformer networks, which can learn the shapes and sizes
of patches from data. The dense transformer networks employ an encoder-decoder
architecture, and a pair of dense transformer modules are inserted into each of
the encoder and decoder paths. The novelty of this work is that we provide
technical solutions for learning the shapes and sizes of patches from data and
efficiently restoring the spatial correspondence required for dense prediction.
The proposed dense transformer modules are differentiable, thus the entire
network can be trained. We apply the proposed networks on natural and
biological image segmentation tasks and show superior performance is achieved
in comparison to baseline methods.