Conditional Convolutions for Instance Segmentation

CondInst is a simple yet effective instance segmentation framework. It eliminates ROI cropping and feature alignment with the instance-aware mask heads. As a result, CondInst can solve instance segmentation with fully convolutional networks. CondInst is able to produce high-resolution instance masks without longer computational time. Extensive experiments show that CondInst can achieve even better performance and inference speed than Mask R-CNN. It can be a strong alternative to previous ROI-based instance segmentation methods. Code is at https://github.com/aim-uofa/AdelaiDet.