Our work focuses on tackling large-scale fine-grained image retrieval as
ranking the images depicting the concept of interests (i.e., the same
sub-category labels) highest based on the fine-grained details in the query. It
is desirable to alleviate the challenges of both fine-grained nature of small
inter-class variations with large intra-class variations and explosive growth
of fine-grained data for such a practical task. In this paper, we propose
attribute-aware hashing networks with self-consistency for generating
attribute-aware hash codes to not only make the retrieval process efficient,
but also establish explicit correspondences between hash codes and visual
attributes. Specifically, based on the captured visual representations by
attention, we develop an encoder-decoder structure network of a reconstruction
task to unsupervisedly distill high-level attribute-specific vectors from the
appearance-specific visual representations without attribute annotations. Our
models are also equipped with a feature decorrelation constraint upon these
attribute vectors to strengthen their representative abilities. Then, driven by
preserving original entities' similarity, the required hash codes can be
generated from these attribute-specific vectors and thus become
attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we
consider the model design from the perspective of the self-consistency
principle and propose to further enhance models' self-consistency by equipping
an additional image reconstruction path. Comprehensive quantitative experiments
under diverse empirical settings on six fine-grained retrieval datasets and two
generic retrieval datasets show the superiority of our models over competing
methods.

本研究提出了一种面向大规模细粒度图像检索的属性感知哈希网络，通过生成属性感知哈希码，既能提高检索效率，又能建立哈希码和视觉属性之间的明确对应关系。在无监督的条件下，通过注意力捕获视觉表征并开发一个编码器 - 解码器结构网络的重构任务，从外观特定的视觉表征中提取高层次的属性特定向量，进一步通过特征去相关性约束来增强它们的代表性。通过保持原始实体的相似性，可以从这些属性特定向量生成所需的哈希码，从而成为属性感知的哈希码。在六个细粒度检索数据集和两个通用检索数据集上进行了全面的定量实验证明了我们模型相对于竞争方法的优越性。