**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.

**Mobile Video Network**, or **MoViNet**, is a type of computation and memory efficient video network that can operate on streaming video for online inference. Three techniques are used to improve efficiency while reducing the peak memory usage of 3D CNNs. First, a video network search space is designed and [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) employed to generate efficient and diverse 3D CNN architectures. Second, a Stream Buffer technique is introduced that decouples memory from video clip duration, allowing 3D CNNs to embed arbitrary-length streaming video sequences for both training and inference with a small constant memory footprint. Third, a simple ensembling technique is used to improve accuracy further without sacrificing efficiency.

MoViNet

MoViNets: Mobile Video Networks for Efficient Video Recognition

Position-Wise Feed-Forward Layer

Attention Is All You Need

**CrossViT** is a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) that uses a dual-branch architecture to extract multi-scale feature representations for image classification. The architecture combines image patches (i.e. tokens in a [transformer](https://paperswithcode.com/method/transformer)) of different sizes to produce stronger visual features for image classification. It processes small and large patch tokens with two separate branches of different computational complexities and these tokens are fused together multiple times to complement each other.

Fusion is achieved by an efficient [cross-attention module](https://paperswithcode.com/method/cross-attention-module), in which each transformer branch creates a non-patch token as an agent to exchange information with the other branch by attention. This allows for linear-time generation of the attention map in fusion instead of quadratic time otherwise.

Source	Attention Is All You Need
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com