  This paper aims for generic instance search from one example where the
instance can be an arbitrary object like shoes, not just near-planar and
one-sided instances like buildings and logos. First, we evaluate
state-of-the-art instance search methods on this problem. We observe that what
works for buildings loses its generality on shoes. Second, we propose to use
automatically learned category-specific attributes to address the large
appearance variations present in generic instance search. Searching among
instances from the same category as the query, the category-specific attributes
outperform existing approaches by a large margin on shoes and cars and perform
on par with the state-of-the-art on buildings. Third, we treat person
re-identification as a special case of generic instance search. On the popular
VIPeR dataset, we reach state-of-the-art performance with the same method.
Fourth, we extend our method to search objects without restriction to the
specifically known category. We show that the combination of category-level
information and the category-specific attributes is superior to the alternative
method combining category-level information with low-level features such as
Fisher vector.
