  In this paper, an adversarial erasing embedding network with the guidance of
high-order attributes (AEEN-HOA) is proposed for going further to solve the
challenging ZSL/GZSL task. AEEN-HOA consists of two branches, i.e., the upper
stream is capable of erasing some initially discovered regions, then the
high-order attribute supervision is incorporated to characterize the
relationship between the class attributes. Meanwhile, the bottom stream is
trained by taking the current background regions to train the same attribute.
As far as we know, it is the first time of introducing the erasing operations
into the ZSL task. In addition, we first propose a class attribute activation
map for the visualization of ZSL output, which shows the relationship between
class attribute feature and attention map. Experiments on four standard
benchmark datasets demonstrate the superiority of AEEN-HOA framework.
