**IFNet** is an architecture for video frame interpolation that adopts a coarse-to-fine strategy with progressively increased resolutions: it iteratively updates intermediate flows and soft fusion mask via successive [IFBlocks](https://paperswithcode.com/method/ifblock). Conceptually, according to the iteratively updated flow fields, we can move corresponding pixels from two input frames to the same location in a latent intermediate frame and use a fusion mask to combine pixels from two input frames. Unlike most previous optical flow models, IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 [convolution](https://paperswithcode.com/method/convolution) and deconvolution as building blocks.

The **NVAE Generative Residual Cell** is a skip connection block used as part of the [NVAE](https://paperswithcode.com/method/nvae) architecture for the generator. The residual cell expands the number of channels $E$ times before applying the [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution), and then maps it back to $C$ channels. The design motivation was to help model long-range correlations in the data by increasing the receptive field of the network, which explains the expanding path but also the use of depthwise convolutions to keep a handle on parameter count.

NVAE Generative Residual Cell

NVAE: A Deep Hierarchical Variational Autoencoder

IFNet

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

**FoveaBox** is anchor-free framework for object detection. Instead of using predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations for each input image

It is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-shelf convolutional network. The first subnet performs per pixel classification on the backbone’s output; the second subnet performs bounding box prediction for the corresponding
position.

Source	RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com