论文标题
隐式特征金字塔网络用于对象检测
Implicit Feature Pyramid Network for Object Detection
论文作者
论文摘要
在本文中，我们提出了一个隐式特征金字塔网络（I-FPN），以进行对象检测。现有的FPN堆叠了几个跨尺度块，以获得大型接受场。我们建议使用最近在深层平衡模型（DEQ）中引入的隐式函数来对FPN的转换进行建模。我们开发了一个剩余的迭代，以有效地更新隐藏状态。 MS COCO数据集的实验结果表明，与具有Resnet-50-FPN的基线检测器相比，I-FPN可以显着提高检测性能：+3.4，+3.2，+3.2，+3.5，+4.2，+3.2在视网膜，更快的RCNN，FCOS，FCOS，ATSS，ATSS和AUTOASIGN上的映射。
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.
