  Computer vision is difficult, partly because the desired mathematical
function connecting input and output data is often complex, fuzzy and thus hard
to learn. Coarse-to-fine (C2F) learning is a promising direction, but it
remains unclear how it is applied to a wide range of vision problems.
  This paper presents a generalized C2F framework by making two technical
contributions. First, we provide a unified way of C2F propagation, in which the
coarse prediction (a class vector, a detected box, a segmentation mask, etc.)
is encoded into a dense (pixel-level) matrix and concatenated to the original
input, so that the fine model takes the same design of the coarse model but
sees additional information. Second, we present a progressive training strategy
which starts with feeding the ground-truth instead of the coarse output into
the fine model, and gradually increases the fraction of coarse output, so that
at the end of training the fine model is ready for testing. We also relate our
approach to curriculum learning by showing that data difficulty keeps
increasing during the training process. We apply our framework to three vision
tasks including image classification, object localization and semantic
segmentation, and demonstrate consistent accuracy gain compared to the baseline
training strategy.
