  We address zero-shot (ZS) learning, building upon prior work in hierarchical
classification by combining it with approaches based on semantic attribute
estimation. For both non-novel and novel image classes we compare multiple
formulations of the problem, starting with deep universal features in each
case. We investigate the effect of using different posterior probabilities as
inputs to the hierarchical classifier, comparing the performances of posteriors
derived from distances to SVM classifier boundaries with those of posteriors
based on semantic attribute estimation. Using a dataset consisting of 150
object classes from the ImageNet ILSVRC2012 data set, we find that the
hierarchical classification method that maximizes expected reward for non-novel
classes differs from the method that maximizes expected reward for novel
classes. We also show that using input posteriors based on semantic attributes
improves the expected reward for novel classes.

Computer Vision and Pattern Recognition