Abstract:
Modeling implicit feature interaction patterns is of significant importance
to object detection tasks. However, in the two-stage detectors, due to the
excessive use of hand-crafted components, it is very difficult to reason about
the implicit relationship of the instance features. To tackle this problem, we
analyze three different levels of feature interaction relationships, namely,
the dependency relationship between the cropped local features and global
features, the feature autocorrelation within the instance, and the
cross-correlation relationship between the instances. To this end, we propose a
more compact object detector head network (CODH), which can not only preserve
global context information and condense the information density, but also
allows instance-wise feature enhancement and relational reasoning in a larger
matrix space. Without bells and whistles, our method can effectively improve
the detection performance while significantly reducing the parameters of the
model, e.g., with our method, the parameters of the head network is 0.6 times
smaller than the state-of-the-art Cascade R-CNN, yet the performance boost is
1.3% on COCO test-dev. Without losing generality, we can also build a more
lighter head network for other multi-stage detectors by assembling our method.
