  Multi-label zero-shot classification aims to predict multiple unseen class
labels for an input image. It is more challenging than its single-label
counterpart. On one hand, the unconstrained number of labels assigned to each
image makes the model more easily overfit to those seen classes. On the other
hand, there is a large semantic gap between seen and unseen classes in the
existing multi-label classification datasets. To address these difficult
issues, this paper introduces a novel multi-label zero-shot classification
framework by learning to transfer from external knowledge. We observe that
ImageNet is commonly used to pretrain the feature extractor and has a large and
fine-grained label space. This motivates us to exploit it as external knowledge
to bridge the seen and unseen classes and promote generalization. Specifically,
we construct a knowledge graph including not only classes from the target
dataset but also those from ImageNet. Since ImageNet labels are not available
in the target dataset, we propose a novel PosVAE module to infer their initial
states in the extended knowledge graph. Then we design a relational graph
convolutional network (RGCN) to propagate information among classes and achieve
knowledge transfer. Experimental results on two benchmark datasets demonstrate
the effectiveness of the proposed approach.
