SAN: Learning Relationship between
Convolutional Features
for Multi-Scale Object Detection
Abstract. Most of the recent successful methods in accurate object
detection build on the convolutional neural networks (CNN). However,
due to the lack of scale normalization in CNN-based detection methods,
the activated channels in the feature space can be completely different
according to a scale and this difference makes it hard for the classi-
fier to learn samples. We propose a Scale Aware Network (SAN) that
maps the convolutional features from the different scales onto a scaleinvariant subspace to make CNN-based detection methods more robust
to the scale variation, and also construct a unique learning method which
considers purely the relationship between channels without the spatial
information for the efficient learning of SAN. To show the validity of our
method, we visualize how convolutional features change according to
the scale through a channel activation matrix and experimentally show
that SAN reduces the feature differences in the scale space. We evaluate
our method on VOC PASCAL and MS COCO dataset. We demonstrate
SAN by conducting several experiments on structures and parameters.
The proposed SAN can be generally applied to many CNN-based detection methods to enhance the detection accuracy with a slight increase in
the computing time