  Zero-shot learning (ZSL) endows the computer vision system with the
inferential capability to recognize instances of a new category that has never
seen before. Two fundamental challenges in it are visual-semantic embedding and
domain adaptation in cross-modality learning and unseen class prediction steps,
respectively. To address both challenges, this paper presents two corresponding
methods named Adaptive STructural Embedding (ASTE) and Self-PAsed Selective
Strategy (SPASS), respectively. Specifically, ASTE formulates the
visualsemantic interactions in a latent structural SVM framework to adaptively
adjust the slack variables to embody the different reliableness among training
instances. In this way, the reliable instances are imposed with small
punishments, wheras the less reliable instances are imposed with more severe
punishments. Thus, it ensures a more discriminative embedding. On the other
hand, SPASS offers a framework to alleviate the domain shift problem in ZSL,
which exploits the unseen data in an easy to hard fashion. Particularly, SPASS
borrows the idea from selfpaced learning by iteratively selecting the unseen
instances from reliable to less reliable to gradually adapt the knowledge from
the seen domain to the unseen domain. Subsequently, by combining SPASS and
ASTE, we present a self-paced Transductive ASTE (TASTE) method to progressively
reinforce the classification capacity. Extensive experiments on three benchmark
datasets (i.e., AwA, CUB, and aPY) demonstrate the superiorities of ASTE and
TASTE. Furthermore, we also propose a fast training (FT) strategy to improve
the efficiency of most of existing ZSL methods. The FT strategy is surprisingly
simple and general enough, which can speed up the training time of most
existing methods by 4~300 times while holding the previous performance.
