  Feature pyramids have become ubiquitous in multi-scale computer vision tasks
such as object detection. Given their importance, a computer vision network can
be divided into three parts: a backbone (generating a feature pyramid), a neck
(refining the feature pyramid) and a head (generating the final output). Many
existing networks operating on feature pyramids, named necks, are shallow and
mostly focus on communication-based processing in the form of top-down and
bottom-up operations. We present a new neck architecture called Trident Pyramid
Network (TPN), that allows for a deeper design and for a better balance between
communication-based processing and self-processing. We show consistent
improvements when using our TPN neck on the COCO object detection benchmark,
outperforming the popular BiFPN baseline by 0.5 AP, both when using the
ResNet-50 and the ResNeXt-101-DCN backbone. Additionally, we empirically show
that it is more beneficial to put additional computation into the TPN neck,
rather than into the backbone, by outperforming a ResNet-101+FPN baseline with
our ResNet-50+TPN network by 1.7 AP, while operating under similar computation
budgets. This emphasizes the importance of performing computation at the
feature pyramid level in modern-day object detection systems. Code is available
at https://github.com/CedricPicron/TPN .
