  Cross-modal retrieval aims to enable flexible retrieval experience by
combining multimedia data such as image, video, text, and audio. One core of
unsupervised approaches is to dig the correlations among different object
representations to complete satisfied retrieval performance without requiring
expensive labels. In this paper, we propose a Graph Pattern Loss based
Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval to
deeply analyze correlations among representations. First, we propose a
diversified attention feature projector by considering the interaction between
different representations to generate multiple representations of an instance.
Then, we design a novel graph pattern loss to explore the correlations among
different representations, in this graph all possible distances between
different representations are considered. In addition, a modality classifier is
added to explicitly declare the corresponding modalities of features before
fusion and guide the network to enhance discrimination ability. We test GPLDAN
on four public datasets. Compared with the state-of-the-art cross-modal
retrieval methods, the experimental results demonstrate the performance and
competitiveness of GPLDAN.
