  Cascade is a classic yet powerful architecture that has boosted performance
on various tasks. However, how to introduce cascade to instance segmentation
remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN
only brings limited gain. In exploring a more effective approach, we find that
the key to a successful instance segmentation cascade is to fully leverage the
reciprocal relationship between detection and segmentation. In this work, we
propose a new framework, Hybrid Task Cascade (HTC), which differs in two
important aspects: (1) instead of performing cascaded refinement on these two
tasks separately, it interweaves them for a joint multi-stage processing; (2)
it adopts a fully convolutional branch to provide spatial context, which can
help distinguishing hard foreground from cluttered background. Overall, this
framework can learn more discriminative features progressively while
integrating complementary features together in each stage. Without bells and
whistles, a single HTC obtains 38.4 and 1.5 improvement over a strong Cascade
Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves
48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018
Challenge Object Detection Task. Code is available at:
https://github.com/open-mmlab/mmdetection.
