  Visual feature pyramid has shown its superiority in both effectiveness and
efficiency in a wide range of applications. However, the existing methods
exorbitantly concentrate on the inter-layer feature interactions but ignore the
intra-layer feature regulations, which are empirically proved beneficial.
Although some methods try to learn a compact intra-layer feature representation
with the help of the attention mechanism or the vision transformer, they ignore
the neglected corner regions that are important for dense prediction tasks. To
address this problem, in this paper, we propose a Centralized Feature Pyramid
(CFP) for object detection, which is based on a globally explicit centralized
feature regulation. Specifically, we first propose a spatial explicit visual
center scheme, where a lightweight MLP is used to capture the globally
long-range dependencies and a parallel learnable visual center mechanism is
used to capture the local corner regions of the input images. Based on this, we
then propose a globally centralized regulation for the commonly-used feature
pyramid in a top-down fashion, where the explicit visual center information
obtained from the deepest intra-layer feature is used to regulate frontal
shallow features. Compared to the existing feature pyramids, CFP not only has
the ability to capture the global long-range dependencies, but also efficiently
obtain an all-round yet discriminative feature representation. Experimental
results on the challenging MS-COCO validate that our proposed CFP can achieve
the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX
object detection baselines.
