**SKNet** is a type of convolutional neural network that employs [selective kernel](https://paperswithcode.com/method/selective-kernel) units, with selective kernel convolutions, in its architecture. This allows for a type of attention where the network can learn to attend to different receptive fields.

**TimeSformer** is a [convolution](https://paperswithcode.com/method/convolution)-free approach to video classification built exclusively on self-attention over space and time. It adapts the standard [Transformer](https://paperswithcode.com/method/transformer) architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Specifically, the method adapts the image model [[Vision Transformer](https://paperswithcode.com/method/vision-transformer)](https//www.paperswithcode.com/method/vision-transformer) (ViT) to video by extending the self-attention mechanism from the image space to the space-time 3D volume. As in ViT, each patch is linearly mapped into an embedding and augmented with positional information. This makes it possible to interpret the resulting sequence of vector

TimeSformer

Is Space-Time Attention All You Need for Video Understanding?

SKNet

The **Self-Organizing Map (SOM)**, commonly also known as Kohonen network (Kohonen 1982, Kohonen 2001) is a computational method for the visualization and analysis of high-dimensional data, especially experimentally acquired information.

Extracted from [scholarpedia](http://www.scholarpedia.org/article/Self-organizing_map)

**Sources**:

Image: [scholarpedia](http://www.scholarpedia.org/article/File:Somnbc.png)

Paper: [Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)](https://doi.org/10.1007/BF00337288)

Book: [Self-Organizing Maps](https://doi.org/10.1007/978-3-642-56927-2)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com