  Existing deep hashing approaches fail to fully explore semantic correlations
and neglect the effect of linguistic context on visual attention learning,
leading to inferior performance. This paper proposes a dual-stream learning
framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs
a discriminative common discrete space by collaboratively incorporating the
shared and individual semantics deduced from visual features and semantic
labels. Specifically, the context-aware representations are generated by
employing the outer product of visual embeddings and semantic encodings.
Moreover, we reconstruct the labels and introduce the focal loss to take
advantage of frequent and rare concepts. The common binary code space is built
on the joint learning of the visual representations attended by language, the
semantic-invariant structure construction and the label distribution
correction. Extensive experiments demonstrate the superiority of our method.
