  Multi-label zero-shot learning extends conventional single-label zero-shot
learning to a more realistic scenario that aims at recognizing multiple unseen
labels of classes for each input sample. Existing works usually exploit
attention mechanism to generate the correlation among different labels.
However, most of them are usually biased on several major classes while neglect
most of the minor classes with the same importance in input samples, and may
thus result in overly diffused attention maps that cannot sufficiently cover
minor classes. We argue that disregarding the connection between major and
minor classes, i.e., correspond to the global and local information,
respectively, is the cause of the problem. In this paper, we propose a novel
framework of unbiased multi-label zero-shot learning, by considering various
class-specific regions to calibrate the training process of the classifier.
Specifically, Pyramid Feature Attention (PFA) is proposed to build the
correlation between global and local information of samples to balance the
presence of each class. Meanwhile, for the generated semantic representations
of input samples, we propose Semantic Attention (SA) to strengthen the
element-wise correlation among these vectors, which can encourage the
coordinated representation of them. Extensive experiments on the large-scale
multi-label zero-shot benchmarks NUS-WIDE and Open-Image demonstrate that the
proposed method surpasses other representative methods by significant margins.
