  The demand for large-scale trademark retrieval (TR) systems has significantly
increased to combat the rise in international trademark infringement.
Unfortunately, the ranking accuracy of current approaches using either
hand-crafted or pre-trained deep convolution neural network (DCNN) features is
inadequate for large-scale deployments. We show in this paper that the ranking
accuracy of TR systems can be significantly improved by incorporating hard and
soft attention mechanisms, which direct attention to critical information such
as figurative elements and reduce attention given to distracting and
uninformative elements such as text and background. Our proposed approach
achieves state-of-the-art results on a challenging large-scale trademark
dataset.
