  In recent years, hashing methods have been popular in the large-scale media
search for low storage and strong representation capabilities. To describe
objects with similar overall appearance but subtle differences, more and more
studies focus on hashing-based fine-grained image retrieval. Existing hashing
networks usually generate both local and global features through attention
guidance on the same deep activation tensor, which limits the diversity of
feature representations. To handle this limitation, we substitute convolutional
descriptors for attention-guided features and propose an Attributes Grouping
and Mining Hashing (AGMH), which groups and embeds the category-specific visual
attributes in multiple descriptors to generate a comprehensive feature
representation for efficient fine-grained image retrieval. Specifically, an
Attention Dispersion Loss (ADL) is designed to force the descriptors to attend
to various local regions and capture diverse subtle details. Moreover, we
propose a Stepwise Interactive External Attention (SIEA) to mine critical
attributes in each descriptor and construct correlations between fine-grained
attributes and objects. The attention mechanism is dedicated to learning
discrete attributes, which will not cost additional computations in hash codes
generation. Finally, the compact binary codes are learned by preserving
pairwise similarities. Experimental results demonstrate that AGMH consistently
yields the best performance against state-of-the-art methods on fine-grained
benchmark datasets.
