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In conventional object detection frameworks, a backbone body inherited fromimage recognition models extracts deep latent features and then a neck modulefuses these latent features to capture information at different scales. As theresolution in object detection is much larger than in image recognition, thecomputational cost of the backbone often dominates the total inference cost.This heavy-backbone design paradigm is mostly due to the historical legacy whentransferring image recognition models to object detection rather than anend-to-end optimized design for object detection. In this work, we show thatsuch paradigm indeed leads to sub-optimal object detection models. To this end,we propose a novel heavy-neck paradigm, GiraffeDet, a giraffe-like network forefficient object detection. The GiraffeDet uses an extremely lightweightbackbone and a very deep and large neck module which encourages denseinformation exchange among different spatial scales as well as different levelsof latent semantics simultaneously. This design paradigm allows detectors toprocess the high-level semantic information and low-level spatial informationat the same priority even in the early stage of the network, making it moreeffective in detection tasks. Numerical evaluations on multiple popular objectdetection benchmarks show that GiraffeDet consistently outperforms previousSOTA models across a wide spectrum of resource constraints. The source code isavailable at https://github.com/jyqi/GiraffeDet.

GiraffeDet: A Heavy-Neck Paradigm for Object Detection