**Receptive Field Block (RFB)** is a module for strengthening the deep features learned from lightweight CNN models so that they can contribute to fast and accurate detectors. Specifically, RFB makes use of multi-branch pooling with varying kernels corresponding to RFs of different sizes, applies [dilated convolution](https://paperswithcode.com/method/dilated-convolution) layers to control their eccentricities, and reshapes them to generate
final representation.

A Gsop block has a squeeze module and an excitation module, and uses a second-order pooling to model high-order statistics while gathering global information.
In the squeeze module, a GSoP block firstly reduces the number of channels from $c$ to $c'$ ($c' < c$) using a $1 \times 1$ convolution,  then  computes a $c' \times c'$ covariance matrix for the different channels to obtain their correlation.  Next, row-wise normalization is performed on the covariance matrix.  Each $(i, j)$ in the normalized covariance matrix explicitly relates channel $i$ to channel $j$. 

In the excitation module, a GSoP block performs row-wise convolution to  maintain structural information and output a vector. Then a fully-connected layer and a sigmoid function are applied  to get a $c$-dimensional attention vector. Finally, it multiplies the input features by the attention vector, as in an SE block. A GSoP block can be formulated as:
\begin{align}
    s = F_\text{gsop}(X, \theta) & = \sigma (W \text{RC}(\text{Cov}(\text{Conv}(X))))
\end{align}
\begin{align}
    Y & = s  X
\end{align}
Here, $\text{Conv}(\cdot)$ reduces the number of channels,
$\text{Cov}(\cdot)$ computes the covariance matrix and
$\text{RC}(\cdot)$ means row-wise convolution.

GSoP-Net

Global Second-order Pooling Convolutional Networks

Receptive Field Block Net for Accurate and Fast Object Detection

A **Wide Residual Block** is a type of [residual block](https://paperswithcode.com/method/residual-block) that utilises two conv 3x3 layers (with [dropout](https://paperswithcode.com/method/dropout)). This is wider than other variants of residual blocks (for instance [bottleneck residual blocks](https://paperswithcode.com/method/bottleneck-residual-block)). It was proposed as part of the [WideResNet](https://paperswithcode.com/method/wideresnet) CNN architecture.

Source	Receptive Field Block Net for Accurate and Fast Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com