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.

The **Pointer Sentinel-LSTM mixture model** is a type of recurrent neural network that combines the advantages of standard [softmax](https://paperswithcode.com/method/softmax) classifiers with those of a pointer component for effective and efficient language modeling. Rather than relying on the RNN hidden state to decide when to use the pointer, the model allows the pointer component itself to decide when to use the softmax vocabulary through a sentinel.

Pointer Sentinel-LSTM

Pointer Sentinel Mixture Models

Channel Squeeze and Spatial Excitation

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

**BASNet**, or **Boundary-Aware Segmentation Network**, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.  The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual 
 refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.

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