  We extend the state-of-the-art Cascade R-CNN with a simple feature sharing
mechanism. Our approach focuses on the performance increases on high IoU but
decreases on low IoU thresholds--a key problem this detector suffers from.
Feature sharing is extremely helpful, our results show that given this
mechanism embedded into all stages, we can easily narrow the gap between the
last stage and preceding stages on low IoU thresholds without resorting to the
commonly used testing ensemble but the network itself. We also observe obvious
improvements on all IoU thresholds benefited from feature sharing, and the
resulting cascade structure can easily match or exceed its counterparts, only
with negligible extra parameters introduced. To push the envelope, we
demonstrate 43.2 AP on COCO object detection without any bells and whistles
including testing ensemble, surpassing previous Cascade R-CNN by a large
margin. Our framework is easy to implement and we hope it can serve as a
general and strong baseline for future research.
