Instance and Panoptic Segmentation Using Conditional Convolutions

@article{Tian2021InstanceAP,
  title={Instance and Panoptic Segmentation Using Conditional Convolutions},
  author={Zhi Tian and Bowen Zhang and Hao Chen and Chunhua Shen},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  volume={45},
  pages={669-680}
}
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… 

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