  In the CNN based object detectors, feature pyramids are widely exploited to
alleviate the problem of scale variation across object instances. These object
detectors, which strengthen features via a top-down pathway and lateral
connections, are mainly to enrich the semantic information of low-level
features, but ignore the enhancement of high-level features. This can lead to
an imbalance between different levels of features, in particular a serious lack
of detailed information in the high-level features, which makes it difficult to
get accurate bounding boxes. In this paper, we introduce a novel two-pronged
transductive idea to explore the relationship among different layers in both
backward and forward directions, which can enrich the semantic information of
low-level features and detailed information of high-level features at the same
time. Under the guidance of the two-pronged idea, we propose a Two-Pronged
Network (TPNet) to achieve bidirectional transfer between high-level features
and low-level features, which is useful for accurately detecting object at
different scales. Furthermore, due to the distribution imbalance between the
hard and easy samples in single-stage detectors, the gradient of localization
loss is always dominated by the hard examples that have poor localization
accuracy. This will enable the model to be biased toward the hard samples. So
in our TPNet, an adaptive IoU based localization loss, named Rectified IoU
(RIoU) loss, is proposed to rectify the gradients of each kind of samples. The
Rectified IoU loss increases the gradients of examples with high IoU while
suppressing the gradients of examples with low IoU, which can improve the
overall localization accuracy of model. Extensive experiments demonstrate the
superiority of our TPNet and RIoU loss.
