SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval. (arXiv:2209.13833v1 [cs.CV])

In this paper, we propose Suppression-Enhancing Mask based attention and
Interactive Channel transformatiON (SEMICON) to learn binary hash codes for
dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we
first develop a suppression-enhancing mask (SEM) based attention to dynamically
localize discriminative image regions. More importantly, different from
existing attention mechanism simply erasing previous discriminative regions,
our SEM is developed to restrain such regions and then discover other
complementary regions by considering the relation between activated regions in
a stage-by-stage …

arxiv fine-grained hash image retrieval scale solution