  Logo detection in unconstrained images is challenging, particularly when only
very sparse labelled training images are accessible due to high labelling
costs. In this work, we describe a model training image synthesising method
capable of improving significantly logo detection performance when only a
handful of (e.g., 10) labelled training images captured in realistic context
are available, avoiding extensive manual labelling costs. Specifically, we
design a novel algorithm for generating Synthetic Context Logo (SCL) training
images to increase model robustness against unknown background clutters,
resulting in superior logo detection performance. For benchmarking model
performance, we introduce a new logo detection dataset TopLogo-10 collected
from top 10 most popular clothing/wearable brandname logos captured in rich
visual context. Extensive comparisons show the advantages of our proposed SCL
model over the state-of-the-art alternatives for logo detection using two
real-world logo benchmark datasets: FlickrLogo-32 and our new TopLogo-10.
