**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.

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).

Residual GRU

Full Resolution Image Compression with Recurrent Neural Networks

On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

**Sample Consistency Network (SCNet)** is a method for instance segmentation which ensures the IoU distribution of the samples at training time are as close to that at inference time. To this end, only the outputs of the last box stage are used for mask predictions at both training and inference. The Figure shows the IoU distribution of the samples going to the mask branch at training time with/without sample consistency compared to that at inference time.

Source	On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Circular Smooth Label?

Viet-Anh on Software