Panoptic FPN
Panoptic FPN
A **Panoptic FPN** is an extension of an [FPN](https://paperswithcode.com/method/fpn) that can generate both instance and semantic segmentations via FPN. The approach starts with an FPN backbone and adds a branch for performing semantic segmentation in parallel with the existing region-based branch for instance segmentation. No changes are made to the FPN backbone when adding the dense-prediction branch, making it compatible with existing instance segmentation methods. 

The new semantic segmentation branch achieves its goal as follows. Starting from the deepest FPN level (at 1/32 scale), we perform three upsampling stages to yield a feature map at 1/4 scale, where each upsampling stage consists of 3×3 [convolution](https://paperswithcode.com/method/convolution), group norm, [ReLU](https://paperswithcode.com/method/relu), and 2× bilinear upsampling. This strategy is repeated for FPN scales 1/16, 1/8, and 1/4 (with progressively fewer upsampling stages). The result is a set of feature maps at the same 1/4 scale, which are then element-wise summed. A final 1×1 convolution, 4× bilinear upsampling, and [softmax](https://paperswithcode.com/method/softmax) are used to generate the per-pixel class labels at the original image resolution. In addition to stuff classes, this branch also outputs a special ‘other’ class for all pixels belonging to objects (to avoid predicting stuff classes for such pixels).
Panoptic Feature Pyramid Networks
https://paperswithcode.com/paper/panoptic-feature-pyramid-networks
Panoptic Feature Pyramid Networks