Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency
and generalization in large-scale retrieval scenarios. While considerable
success has been achieved, there still exist urgent limitations. Existing works
ignore the locality relationships of representations and attributes, which have
effective transferability between seeable classes and unseeable classes. Also,
the continuous-value attributes are not fully harnessed. In response, we
conduct a COMprehensive Attribute Exploration for ZSH, named COMAE, which
depicts the relationships from seen classes to unseen ones through three
meticulously designed explorations, i.e., point-wise, pair-wise and class-wise
consistency constraints. By regressing attributes from the proposed attribute
prototype network, COMAE learns the local features that are relevant to the
visual attributes. Then COMAE utilizes contrastive learning to comprehensively
depict the context of attributes, rather than instance-independent
optimization. Finally, the class-wise constraint is designed to cohesively
learn the hash code, image representation, and visual attributes more
effectively. Experimental results on the popular ZSH datasets demonstrate that
COMAE outperforms state-of-the-art hashing techniques, especially in scenarios
with a larger number of unseen label classes.

通过三个精心设计的探索（即，点对点，配对和分级一致性约束），COMprehensive Attribute Exploration（COMAE）绘制了从可见类到不可见类的关系，并利用对比学习全面描绘属性的上下文，最终通过类级约束更有效地学习哈希码，图像表示和视觉属性。在流行的 Zero-shot hashing 数据集上的实验结果表明，COMAE 在特别是具有更多不可见标签类别的场景中优于最先进的哈希技术。