  In most recent years, zero-shot recognition (ZSR) has gained increasing
attention in machine learning and image processing fields. It aims at
recognizing unseen class instances with knowledge transferred from seen
classes. This is typically achieved by exploiting a pre-defined semantic
feature space (FS), i.e., semantic attributes or word vectors, as a bridge to
transfer knowledge between seen and unseen classes. However, due to the absence
of unseen classes during training, the conventional ZSR easily suffers from
domain shift and hubness problems. In this paper, we propose a novel ZSR
learning framework that can handle these two issues well by adaptively
adjusting semantic FS. To the best of our knowledge, our work is the first to
consider the adaptive adjustment of semantic FS in ZSR. Moreover, our solution
can be formulated to a more efficient framework that significantly boosts the
training. Extensive experiments show the remarkable performance improvement of
our model compared with other existing methods.
