**Stacked Hourglass Networks** are a type of convolutional neural network for pose estimation. They are based on the successive steps of pooling and upsampling that are done to produce a final set of predictions.

OODformer is a [transformer](https://paperswithcode.com/method/transformer)-based OOD detection architecture that leverages the contextualization capabilities of the transformer. Incorporating the transformer as the principal feature extractor allows to exploit the object concepts and their discriminate attributes along with their co-occurrence via [visual attention](https://paperswithcode.com/method/visual-attention). 

OODformer employs [ViT](method/vision-transformer) and its data efficient variant [DeiT](/method/deit). Each encoder layer consist of multi-head self attention and a multi-layer perception block. The combination of MSA and MLP layers in the encoder jointly encode the attributes' importance, associated correlation, and co-occurrence. The [class] token (a representative of an image $x$) consolidated multiple attributes and their related features via the global context. The [class] token from the final layer is used for OOD detection in two ways; first, it is passed to $
F_{\text {classifier }}\left(x_{\text {feat }}\right)$  for softmax confidence score, and second it is used for latent space distance calculation.

OODformer

OODformer: Out-Of-Distribution Detection Transformer

Stacked Hourglass Network

Stacked Hourglass Networks for Human Pose Estimation

A **Residual GRU** is a [gated recurrent unit (GRU)](https://paperswithcode.com/method/gru) that incorporates the idea of residual connections from [ResNets](https://paperswithcode.com/method/resnet).

Source	Stacked Hourglass Networks for Human Pose Estimation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com