A **Panoptic FPN** is a computer vision technique that is used to perform both instance segmentation and semantic segmentation of an image. It is an extension of the popular FPN algorithm, which uses a feature pyramid to detect and segment objects in an image. The Panoptic FPN adds a new branch for performing semantic segmentation, which allows it to recognize both objects and the background in an image.
What is FPN?
FPN (Feature Pyramid Network) is a popular computer vision technique that is used to detect and segment objects in an image. It was introduced in a 2017 paper by Lin et al. at Facebook AI Research. The FPN algorithm uses a feature pyramid, which is a hierarchy of multi-scale feature maps that are created by feeding an image through a convolutional neural network (CNN).
At each level of the feature pyramid, the algorithm detects and segments objects of different scales. The low-level feature maps are used to detect smaller objects, while the high-level feature maps are used to detect larger objects. By combining information from all levels of the feature pyramid, the algorithm can accurately segment objects of all sizes.
What is Panoptic FPN?
The Panoptic FPN is an extension of the FPN algorithm that can generate both instance and semantic segmentations via FPN. It was introduced in a 2019 paper by Kirillov et al. at Facebook AI Research.
The approach of Panoptic FPN 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.
How does Panoptic FPN work?
The new semantic segmentation branch in Panoptic FPN achieves its goal by performing three upsampling stages to yield a feature map at 1/4 scale, starting from the deepest FPN level at 1/32 scale. Each upsampling stage consists of 3×3 convolution, group norm, 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 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).
Why is Panoptic FPN important?
The Panoptic FPN is an important development in computer vision because it can accurately segment both objects and the background in an image. This is useful for a variety of applications, including object detection, image segmentation, and scene understanding.
The ability to perform both instance and semantic segmentation simultaneously is particularly important for applications like autonomous vehicles, where it is important to accurately recognize not only other vehicles and pedestrians, but also the road and other features of the environment.
The Panoptic FPN is an important extension of the FPN algorithm that can perform both instance and semantic segmentations. It uses a feature pyramid to accurately detect and segment objects of all sizes in an image. This technique has many applications in computer vision, including object detection, image segmentation, and scene understanding.
