  Scale variation is one of the key challenges in object detection. In this
work, we first present a controlled experiment to investigate the effect of
receptive fields for scale variation in object detection. Based on the findings
from the exploration experiments, we propose a novel Trident Network
(TridentNet) aiming to generate scale-specific feature maps with a uniform
representational power. We construct a parallel multi-branch architecture in
which each branch shares the same transformation parameters but with different
receptive fields. Then, we adopt a scale-aware training scheme to specialize
each branch by sampling object instances of proper scales for training. As a
bonus, a fast approximation version of TridentNet could achieve significant
improvements without any additional parameters and computational cost compared
with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101
backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are
available at https://git.io/fj5vR.
