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

Trident Pyramid Networks: The importance of processing at the feature pyramid level for better object detection