  Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on auxiliary data. Reusing the learned mapping to
project target videos into an embedding space thus allows novel-classes to be
recognised by nearest neighbour inference. However, existing ZSL methods suffer
from auxiliary-target domain shift intrinsically induced by assuming the same
mapping for the disjoint auxiliary and target classes. This compromises the
generalisation accuracy of ZSL recognition on the target data. In this work, we
improve the ability of ZSL to generalise across this domain shift in both
model- and data-centric ways by formulating a visual-semantic mapping with
better generalisation properties and a dynamic data re-weighting method to
prioritise auxiliary data that are relevant to the target classes.
Specifically: (1) We introduce a multi-task visual-semantic mapping to improve
generalisation by constraining the semantic mapping parameters to lie on a
low-dimensional manifold, (2) We explore prioritised data augmentation by
expanding the pool of auxiliary data with additional instances weighted by
relevance to the target domain. The proposed new model is applied to the
challenging zero-shot action recognition problem to demonstrate its advantages
over existing ZSL models.
