Dynamic Feature Pyramid Networks for Object Detection

Mingjian Zhu, Kai Han, Changbin Yu, Yunhe Wang

This paper studies feature pyramid network (FPN), which is a widely used module for aggregating multi-scale feature information in the object detection system. The performance gain in most of the existing works is mainly contributed to the increase of computation burden, especially the floating number operations (FLOPs). In addition, the multi-scale information within each layer in FPN has not been well investigated. To this end, we first introduce an inception FPN in which each layer contains convolution filters with different kernel sizes to enlarge the receptive field and integrate more useful information. Moreover, we point out that not all objects need such a complicated calculation module and propose a new dynamic FPN (DyFPN). Each layer in the DyFPN consists of multiple branches with different computational costs. Specifically, the output features of DyFPN will be calculated by using the adaptively selected branch according to a learnable gating operation. Therefore, the proposed method can provide a more efficient dynamic inference for achieving a better trade-off between accuracy and detection performance. Extensive experiments conducted on benchmarks demonstrate that the proposed DyFPN significantly improves performance with the optimal allocation of computation resources. For instance, replacing the FPN with the inception FPN improves detection accuracy by 1.6 AP using the Faster R-CNN paradigm on COCO minival, and the DyFPN further reduces about 40% of its FLOPs while maintaining similar performance.

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