  Large-scale fine-grained image retrieval has two main problems. First, low
dimensional feature embedding can fasten the retrieval process but bring
accuracy reduce due to overlooking the feature of significant attention regions
of images in fine-grained datasets. Second, fine-grained images lead to the
same category query hash codes mapping into the different cluster in database
hash latent space. To handle these two issues, we propose a feature consistency
driven attention erasing network (FCAENet) for fine-grained image retrieval.
For the first issue, we propose an adaptive augmentation module in FCAENet,
which is selective region erasing module (SREM). SREM makes the network more
robust on subtle differences of fine-grained task by adaptively covering some
regions of raw images. The feature extractor and hash layer can learn more
representative hash code for fine-grained images by SREM. With regard to the
second issue, we fully exploit the pair-wise similarity information and add the
enhancing space relation loss (ESRL) in FCAENet to make the vulnerable relation
stabler between the query hash code and database hash code. We conduct
extensive experiments on five fine-grained benchmark datasets (CUB2011,
Aircraft, NABirds, VegFru, Food101) for 12bits, 24bits, 32bits, 48bits hash
code. The results show that FCAENet achieves the state-of-the-art (SOTA)
fine-grained retrieval performance compared with other methods.
