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).

**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.

Stacked Hourglass Network

Stacked Hourglass Networks for Human Pose Estimation

Residual GRU

Full Resolution Image Compression with Recurrent Neural Networks

**Circular Smooth Label** (CSL) is a classification-based rotation detection technique for arbitrary-oriented object detection. It is used for circularly distributed angle classification and addresses the periodicity of the angle and increases the error tolerance to adjacent angles.

Source	Full Resolution Image Compression with Recurrent Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Residual GRU?

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