**Fourier Contour Embedding** is a text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. However, the mask representation might lead to expensive post-processing, while the point sequence one may have limited capability to model texts with highly-curved shapes. This motivates modeling text instances in the Fourier domain.

**CornerNet** is an object detection model that detects an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single [convolution](https://paperswithcode.com/method/convolution) neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. It also utilises [corner pooling](https://paperswithcode.com/method/corner-pooling), a new type of pooling layer than helps the network better localize corners.

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

Fourier Contour Embedding

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

**IFBlock** is a video model block used in the [IFNet](https://paperswithcode.com/method/ifnet) architecture for video frame interpolation. IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 convolution and deconvolution as building blocks. Each IFBlock has a feed-forward structure consisting of several convolutional layers and an upsampling operator. Except for the layer that outputs the optical flow residuals and the fusion map, [PReLU](https://paperswithcode.com/method/prelu) activations are used.

Source	Fourier Contour Embedding for Arbitrary-Shaped Text Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com