Instance and Panoptic Segmentation Using Conditional Convolutions. (arXiv:2102.03026v5 [cs.CV] UPDATED)

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 …

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