Inspired on the widely known [spatial squeeze and channel excitation (SE)](https://paperswithcode.com/method/squeeze-and-excitation-block) block, the sSE block performs channel squeeze and spatial excitation, to recalibrate the feature maps spatially and achieve more fine-grained image segmentation.

**Seq2Seq**, or **Sequence To Sequence**, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one [LSTM](https://paperswithcode.com/method/lstm), the *encoder*, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the *decoder*, to extract the output sequence
from that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.

(Note that this page refers to the original seq2seq not general sequence-to-sequence models)

Seq2Seq

Sequence to Sequence Learning with Neural Networks

Channel Squeeze and Spatial Excitation

Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

**Region of Interest Warping**, or **RoIWarp**, is a form of [RoIPool](https://paperswithcode.com/method/roi-pooling) that is differentiable with respect to the box position. In practice, this takes the form of a RoIWarp layer followed by a standard [Max Pooling](https://paperswithcode.com/method/max-pooling) layer. The RoIWarp layer crops a feature map region and warps it into a target size by interpolation.

Source	Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com