  Small objects are difficult to detect because of their low resolution and
small size. The existing small object detection methods mainly focus on data
preprocessing or narrowing the differences between large and small objects.
Inspired by human vision "attention" mechanism, we exploit two feature
extraction methods to mine the most useful information of small objects. Both
methods are based on multiresolution feature extraction. We initially design
and explore the soft attention method, but we find that its convergence speed
is slow. Then we present the second method, an attention-based feature
interaction method, called a MultiResolution Attention Extractor (MRAE),
showing significant improvement as a generic feature extractor in small object
detection. After each building block in the vanilla feature extractor, we
append a small network to generate attention weights followed by a weighted-sum
operation to get the final attention maps. Our attention-based feature
extractor is 2.0 times the AP of the "hard" attention counterpart (plain
architecture) on the COCO small object detection benchmark, proving that MRAE
can capture useful location and contextual information through adaptive
learning.
