  Cross-media retrieval of scientific and technological information is one of
the important tasks in the cross-media study. Cross-media scientific and
technological information retrieval obtain target information from massive
multi-source and heterogeneous scientific and technological resources, which
helps to design applications that meet users' needs, including scientific and
technological information recommendation, personalized scientific and
technological information retrieval, etc. The core of cross-media retrieval is
to learn a common subspace, so that data from different media can be directly
compared with each other after being mapped into this subspace. In subspace
learning, existing methods often focus on modeling the discrimination of
intra-media data and the invariance of inter-media data after mapping; however,
they ignore the semantic consistency of inter-media data before and after
mapping and media discrimination of intra-semantics data, which limit the
result of cross-media retrieval. In light of this, we propose a scientific and
technological information oriented Semantics-adversarial and Media-adversarial
Cross-media Retrieval method (SMCR) to find an effective common subspace.
Specifically, SMCR minimizes the loss of inter-media semantic consistency in
addition to modeling intra-media semantic discrimination, to preserve semantic
similarity before and after mapping. Furthermore, SMCR constructs a basic
feature mapping network and a refined feature mapping network to jointly
minimize the media discriminative loss within semantics, so as to enhance the
feature mapping network's ability to confuse the media discriminant network.
Experimental results on two datasets demonstrate that the proposed SMCR
outperforms state-of-the-art methods in cross-media retrieval.
