  This paper addresses cross-domain visual search, where visual queries
retrieve category samples from a different domain. For example, we may want to
sketch an airplane and retrieve photographs of airplanes. Despite considerable
progress, the search occurs in a closed setting between two pre-defined
domains. In this paper, we make the step towards an open setting where multiple
visual domains are available. This notably translates into a search between any
pair of domains, from a combination of domains or within multiple domains. We
introduce a simple -- yet effective -- approach. We formulate the search as a
mapping from every visual domain to a common semantic space, where categories
are represented by hyperspherical prototypes. Open cross-domain visual search
is then performed by searching in the common semantic space, regardless of
which domains are used as source or target. Domains are combined in the common
space to search from or within multiple domains simultaneously. A separate
training of every domain-specific mapping function enables an efficient scaling
to any number of domains without affecting the search performance. We
empirically illustrate our capability to perform open cross-domain visual
search in three different scenarios. Our approach is competitive with respect
to existing closed settings, where we obtain state-of-the-art results on
several benchmarks for three sketch-based search tasks.
