COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Yihang Zhou Qingqing Long Yuchen Yan Xiao Luo Zeyu Dong Xuezhi Wang Zhen Meng Pengfei Wang Yuanchun Zhou
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.