  In this paper, we present an implicit feature pyramid network (i-FPN) for
object detection. Existing FPNs stack several cross-scale blocks to obtain
large receptive field. We propose to use an implicit function, recently
introduced in deep equilibrium model (DEQ), to model the transformation of FPN.
We develop a residual-like iteration to updates the hidden states efficiently.
Experimental results on MS COCO dataset show that i-FPN can significantly boost
detection performance compared to baseline detectors with ResNet-50-FPN: +3.4,
+3.2, +3.5, +4.2, +3.2 mAP on RetinaNet, Faster-RCNN, FCOS, ATSS and
AutoAssign, respectively.
