  Multi-scale features are of great importance in encoding objects with scale
variance in object detection tasks. A common strategy for multi-scale feature
extraction is adopting the classic top-down and bottom-up feature pyramid
networks. However, these approaches suffer from the loss or degradation of
feature information, impairing the fusion effect of non-adjacent levels. This
paper proposes an asymptotic feature pyramid network (AFPN) to support direct
interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent
low-level features and asymptotically incorporates higher-level features into
the fusion process. In this way, the larger semantic gap between non-adjacent
levels can be avoided. Given the potential for multi-object information
conflicts to arise during feature fusion at each spatial location, adaptive
spatial fusion operation is further utilized to mitigate these inconsistencies.
We incorporate the proposed AFPN into both two-stage and one-stage object
detection frameworks and evaluate with the MS-COCO 2017 validation and test
datasets. Experimental evaluation shows that our method achieves more
competitive results than other state-of-the-art feature pyramid networks. The
code is available at
\href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}.
