  Thanks to the efficient retrieval speed and low storage consumption, learning
to hash has been widely used in visual retrieval tasks. However, existing
hashing methods assume that the query and retrieval samples lie in homogeneous
feature space within the same domain. As a result, they cannot be directly
applied to heterogeneous cross-domain retrieval. In this paper, we propose a
Generalized Image Transfer Retrieval (GITR) problem, which encounters two
crucial bottlenecks: 1) the query and retrieval samples may come from different
domains, leading to an inevitable {domain distribution gap}; 2) the features of
the two domains may be heterogeneous or misaligned, bringing up an additional
{feature gap}. To address the GITR problem, we propose an Asymmetric Transfer
Hashing (ATH) framework with its unsupervised/semi-supervised/supervised
realizations. Specifically, ATH characterizes the domain distribution gap by
the discrepancy between two asymmetric hash functions, and minimizes the
feature gap with the help of a novel adaptive bipartite graph constructed on
cross-domain data. By jointly optimizing asymmetric hash functions and the
bipartite graph, not only can knowledge transfer be achieved but information
loss caused by feature alignment can also be avoided. Meanwhile, to alleviate
negative transfer, the intrinsic geometrical structure of single-domain data is
preserved by involving a domain affinity graph. Extensive experiments on both
single-domain and cross-domain benchmarks under different GITR subtasks
indicate the superiority of our ATH method in comparison with the
state-of-the-art hashing methods.
