  Many real-world phenomena are observed at multiple resolutions. Predictive
models designed to predict these phenomena typically consider different
resolutions separately. This approach might be limiting in applications where
predictions are desired at fine resolutions but available training data is
scarce. In this paper, we propose classification algorithms that leverage
supervision from coarser resolutions to help train models on finer resolutions.
The different resolutions are modeled as different views of the data in a
multi-view framework that exploits the complementarity of features across
different views to improve models on both views. Unlike traditional multi-view
learning problems, the key challenge in our case is that there is no one-to-one
correspondence between instances across different views in our case, which
requires explicit modeling of the correspondence of instances across
resolutions. We propose to use the features of instances at different
resolutions to learn the correspondence between instances across resolutions
using an attention mechanism.Experiments on the real-world application of
mapping urban areas using satellite observations and sentiment classification
on text data show the effectiveness of the proposed methods.
