  We propose a novel approach for class-agnostic object proposal generation,
which is efficient and especially well-suited to detect small objects.
Efficiency is achieved by scale-specific objectness attention maps which focus
the processing on promising parts of the image and reduce the amount of sampled
windows strongly. This leads to a system, which is $33\%$ faster than the
state-of-the-art and clearly outperforming state-of-the-art in terms of average
recall. Secondly, we add a module for detecting small objects, which are often
missed by recent models. We show that this module improves the average recall
for small objects by about $53\%$.
