SENet pioneered channel attention. The core of SENet is a squeeze-and-excitation (SE) block which is used to collect global information, capture channel-wise relationships and improve representation ability.
SE blocks are divided into two parts, a squeeze module and an excitation module. Global spatial information is collected in the squeeze module by global average pooling. The excitation module captures channel-wise relationships and outputs an attention vector by using fully-connected layers and non-linear layers (ReLU and sigmoid). Then, each channel of the input feature is scaled by multiplying the corresponding element in the attention vector. Overall, a squeeze-and-excitation block $F_\text{se}$ (with parameter $\theta$) which takes $X$ as input and outputs $Y$ can be formulated 
as:
\begin{align}
    s = F_\text{se}(X, \theta) & = \sigma (W_{2} \delta (W_{1}\text{GAP}(X)))
\end{align}
\begin{align}
    Y = sX
\end{align}

**HEGCN**, or **Hierarchical Entity Graph Convolutional Network** is a model for multi-hop relation extraction across documents. Documents in a document chain are encoded using a bi-directional long short-term memory ([BiLSTM](https://paperswithcode.com/method/bilstm)) layer. On top of the BiLSTM layer, two graph convolutional networks ([GCN](https://paperswithcode.com/method/gcn)) are used, one after another in a hierarchy. 

In the first level of the GCN hierarchy, a separate entity mention graph is constructed on each document of the chain using all the entities mentioned in that document. Each mention of an entity in a document is considered as a separate node in the graph. A graph convolutional network (GCN) is used to represent the entity mention graph of each document to capture the relations among the entity mentions in the document. A unified entity-level graph is then constructed across all the documents in the chain. Each node of this entity-level graph represents a unique entity in the document chain. Each common entity between two documents in the chain is represented by a single node in the graph. A GCN is used to represent this entity-level graph to capture the relations among the entities across the documents. 

The representations of the nodes of the subject entity and object entity are concatenated and passed to a feed-forward layer with [softmax](https://paperswithcode.com/method/softmax) for relation classification.

HEGCN

A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents

Channel attention

Squeeze-and-Excitation Networks

**ESPNet** is a convolutional neural network for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a convolutional module, efficient spatial pyramid ([ESP](https://paperswithcode.com/method/esp)), which is efficient in terms of computation, memory, and power.

Source	Squeeze-and-Excitation Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com