  Cross-modal retrieval has become a highlighted research topic for retrieval
across multimedia data such as image and text. A two-stage learning framework
is widely adopted by most existing methods based on Deep Neural Network (DNN):
The first learning stage is to generate separate representation for each
modality, and the second learning stage is to get the cross-modal common
representation. However, the existing methods have three limitations: (1) In
the first learning stage, they only model intra-modality correlation, but
ignore inter-modality correlation with rich complementary context. (2) In the
second learning stage, they only adopt shallow networks with single-loss
regularization, but ignore the intrinsic relevance of intra-modality and
inter-modality correlation. (3) Only original instances are considered while
the complementary fine-grained clues provided by their patches are ignored. For
addressing the above problems, this paper proposes a cross-modal correlation
learning (CCL) approach with multi-grained fusion by hierarchical network, and
the contributions are as follows: (1) In the first learning stage, CCL exploits
multi-level association with joint optimization to preserve the complementary
context from intra-modality and inter-modality correlation simultaneously. (2)
In the second learning stage, a multi-task learning strategy is designed to
adaptively balance the intra-modality semantic category constraints and
inter-modality pairwise similarity constraints. (3) CCL adopts multi-grained
modeling, which fuses the coarse-grained instances and fine-grained patches to
make cross-modal correlation more precise. Comparing with 13 state-of-the-art
methods on 6 widely-used cross-modal datasets, the experimental results show
our CCL approach achieves the best performance.
