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This paper presents the novel combination of a visual transformer style patchclassifier with saccaded local attention. A novel optimisation paradigm fortraining object models is also presented, rather than the optimisation functionminimising class membership probability error the network is trained toestimate the normalised distance to the centroid of labelled objects. Thisapproach builds a degree of transnational invariance directly into the modeland allows fast saccaded search with gradient ascent to find object centroids.The resulting saccaded visual transformer is demonstrated on human faces.

A Saccaded Visual Transformer for General Object Spotting