  Classifying a visual concept merely from its associated online textual
source, such as a Wikipedia article, is an attractive research topic in
zero-shot learning because it alleviates the burden of manually collecting
semantic attributes. Several recent works have pursued this approach by
exploring various ways of connecting the visual and text domains. This paper
revisits this idea by stepping further to consider one important factor: the
textual representation is usually too noisy for the zero-shot learning
application. This consideration motivates us to design a simple-but-effective
zero-shot learning method capable of suppressing noise in the text.
  More specifically, we propose an $l_{2,1}$-norm based objective function
which can simultaneously suppress the noisy signal in the text and learn a
function to match the text document and visual features. We also develop an
optimization algorithm to efficiently solve the resulting problem. By
conducting experiments on two large datasets, we demonstrate that the proposed
method significantly outperforms the competing methods which rely on online
information sources but without explicit noise suppression. We further make an
in-depth analysis of the proposed method and provide insight as to what kind of
information in documents is useful for zero-shot learning.
