  Zero-shot learning (ZSL) can be formulated as a cross-domain matching
problem: after being projected into a joint embedding space, a visual sample
will match against all candidate class-level semantic descriptions and be
assigned to the nearest class. In this process, the embedding space underpins
the success of such matching and is crucial for ZSL. In this paper, we conduct
an in-depth study on the construction of embedding space for ZSL and posit that
an ideal embedding space should satisfy two criteria: intra-class compactness
and inter-class separability. While the former encourages the embeddings of
visual samples of one class to distribute tightly close to the semantic
description embedding of this class, the latter requires embeddings from
different classes to be well separated from each other. Towards this goal, we
present a simple but effective two-branch network to simultaneously map
semantic descriptions and visual samples into a joint space, on which visual
embeddings are forced to regress to their class-level semantic embeddings and
the embeddings crossing classes are required to be distinguishable by a
trainable classifier. Furthermore, we extend our method to a transductive
setting to better handle the model bias problem in ZSL (i.e., samples from
unseen classes tend to be categorized into seen classes) with minimal extra
supervision. Specifically, we propose a pseudo labeling strategy to
progressively incorporate the testing samples into the training process and
thus balance the model between seen and unseen classes. Experimental results on
five standard ZSL datasets show the superior performance of the proposed method
and its transductive extension.
