  We propose a simple yet effective framework for instance and panoptic
segmentation, termed CondInst (conditional convolutions for instance and
panoptic segmentation). In the literature, top-performing instance segmentation
methods typically follow the paradigm of Mask R-CNN and rely on ROI operations
(typically ROIAlign) to attend to each instance. In contrast, we propose to
attend to the instances with dynamic conditional convolutions. Instead of using
instance-wise ROIs as inputs to the instance mask head of fixed weights, we
design dynamic instance-aware mask heads, conditioned on the instances to be
predicted. CondInst enjoys three advantages: 1.) Instance and panoptic
segmentation are unified into a fully convolutional network, eliminating the
need for ROI cropping and feature alignment. 2.) The elimination of the ROI
cropping also significantly improves the output instance mask resolution. 3.)
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 time per
instance and making the overall inference time almost constant, irrelevant to
the number of instances. We demonstrate a simpler method that can achieve
improved accuracy and inference speed on both instance and panoptic
segmentation tasks. On the COCO dataset, we outperform a few state-of-the-art
methods. We hope that CondInst can be a strong baseline for instance and
panoptic segmentation. Code is available at: https://git.io/AdelaiDet
