  Object detection is a fundamental problem in image understanding. One popular
solution is the R-CNN framework and its fast versions. They decompose the
object detection problem into two cascaded easier tasks: 1) generating object
proposals from images, 2) classifying proposals into various object categories.
Despite that we are handling with two relatively easier tasks, they are not
solved perfectly and there's still room for improvement. In this paper, we push
the "divide and conquer" solution even further by dividing each task into two
sub-tasks. We call the proposed method "CRAFT" (Cascade Region-proposal-network
And FasT-rcnn), which tackles each task with a carefully designed network
cascade. We show that the cascade structure helps in both tasks: in proposal
generation, it provides more compact and better localized object proposals; in
object classification, it reduces false positives (mainly between ambiguous
categories) by capturing both inter- and intra-category variances. CRAFT
achieves consistent and considerable improvement over the state-of-the-art on
object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.
