  We propose a simple yet effective instance segmentation framework, termed
CondInst (conditional convolutions for instance segmentation). Top-performing
instance segmentation methods such as Mask R-CNN rely on ROI operations
(typically ROIPool or ROIAlign) to obtain the final instance masks. In
contrast, we propose to solve instance segmentation from a new perspective.
Instead of using instance-wise ROIs as inputs to a network of fixed weights, we
employ dynamic instance-aware networks, conditioned on instances. CondInst
enjoys two advantages: 1) Instance segmentation is solved by a fully
convolutional network, eliminating the need for ROI cropping and feature
alignment. 2) Due to the much improved capacity of dynamically-generated
conditional convolutions, the mask head can be very compact (e.g., 3 conv.
layers, each having only 8 channels), leading to significantly faster
inference. We demonstrate a simpler instance segmentation method that can
achieve improved performance in both accuracy and inference speed. On the COCO
dataset, we outperform a few recent methods including well-tuned Mask RCNN
baselines, without longer training schedules needed.
  Code is available: https://github.com/aim-uofa/adet
