  Fine-grained classification remains a very challenging problem, because of
the absence of well-labeled training data caused by the high cost of annotating
a large number of fine-grained categories. In the extreme case, given a set of
test categories without any well-labeled training data, the majority of
existing works can be grouped into the following two research directions: 1)
crawl noisy labeled web data for the test categories as training data, which is
dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary
categories with well-labeled training data to the test categories, which
corresponds to zero-shot learning setting. Nevertheless, the above two research
directions still have critical issues to be addressed. For the first direction,
web data have noisy labels and considerably different data distribution from
test data. For the second direction, zero-shot learning is struggling to
achieve compelling results compared with conventional supervised learning. The
issues of the above two directions motivate us to develop a novel approach
which can jointly exploit both noisy web training data from test categories and
well-labeled training data from auxiliary categories. In particular, on one
hand, we crawl web data for test categories as noisy training data. On the
other hand, we transfer the knowledge from auxiliary categories with
well-labeled training data to test categories by virtue of free semantic
information (e.g., word vector) of all categories. Moreover, given the fact
that web data are generally associated with additional textual information
(e.g., title and tag), we extend our method by using the surrounding textual
information of web data as privileged information. Extensive experiments show
the effectiveness of our proposed methods.
