  This paper presents the novel combination of a visual transformer style patch
classifier with saccaded local attention. A novel optimisation paradigm for
training object models is also presented, rather than the optimisation function
minimising class membership probability error the network is trained to
estimate the normalised distance to the centroid of labelled objects. This
approach builds a degree of transnational invariance directly into the model
and allows fast saccaded search with gradient ascent to find object centroids.
The resulting saccaded visual transformer is demonstrated on human faces.
