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.

本文提出了一种双流学习框架，称为深度协作离散哈希（DCDH），该框架通过协同地将视觉特征和语义标签中的共享和个人语义合并，构建了一种判别性离散空间，从而生成上下文感知表示，再通过联合学习视觉表示和语言，添加标签重构和中心焦点损失以建立共同的二进制编码空间。大量实验证明了该方法的优越性。