  Large-scale trademark retrieval is an important content-based image retrieval
task. A recent study shows that off-the-shelf deep features aggregated with
Regional-Maximum Activation of Convolutions (R-MAC) achieve state-of-the-art
results. However, R-MAC suffers in the presence of background clutter/trivial
regions and scale variance, and discards important spatial information. We
introduce three simple but effective modifications to R-MAC to overcome these
drawbacks. First, we propose the use of both sum and max pooling to minimise
the loss of spatial information. We also employ domain-specific unsupervised
soft-attention to eliminate background clutter and unimportant regions.
Finally, we add multi-resolution inputs to enhance the scale-invariance of
R-MAC. We evaluate these three modifications on the million-scale METU dataset.
Our results show that all modifications bring non-trivial improvements, and
surpass previous state-of-the-art results.
