**Region of Interest Align**, or **RoIAlign**, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh quantization of [RoI Pool](https://paperswithcode.com/method/roi-pooling), properly *aligning* the extracted features with the input. To avoid any quantization of the RoI boundaries or bins (using $x/16$ instead of $[x/16]$), RoIAlign uses bilinear interpolation to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and the result is then aggregated (using max or average).

**Slanted Triangular Learning Rates (STLR)** is a learning rate schedule which first linearly increases the learning rate and then linearly decays it, which can be seen in Figure to the right. It is a modification of Triangular Learning Rates, with a short increase and a long decay period.

Slanted Triangular Learning Rates

Universal Language Model Fine-tuning for Text Classification

RoIAlign

Mask R-CNN

**EfficientDet** is a type of object detection model, which utilizes several optimization and backbone tweaks, such as the use of a [BiFPN](https://paperswithcode.com/method/bifpn), and a compound scaling method that uniformly scales the resolution,depth and width for all backbones, feature networks and box/class prediction networks at the same time.

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