**DeepLabv2** is an architecture for semantic segmentation that build on [DeepLab](https://paperswithcode.com/method/deeplab) with an atrous [spatial pyramid pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) scheme. Here we have parallel dilated convolutions with different rates applied in the input feature map, which are then fused together. As objects of the same class can have different sizes in the image, [ASPP](https://paperswithcode.com/method/aspp) helps to account for different object sizes.

CharacterBERT is a variant of [BERT](https://paperswithcode.com/method/bert) that **drops the wordpiece system** and **replaces it with a CharacterCNN module** just like the one [ELMo](https://paperswithcode.com/method/elmo) uses to produce its first layer representation. This allows CharacterBERT to represent any input token without splitting it into wordpieces. Moreover, this frees BERT from the burden of a domain-specific wordpiece vocabulary which may not be suited to your domain of interest (e.g. medical domain). Finally, it allows the model to be more robust to noisy inputs.

CharacterBERT

CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters

DeepLabv2

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Liu et al. presented self-calibrated convolution as a means to enlarge the receptive field at each spatial location. 

Self-calibrated convolution is used together with a standard convolution. It first divides the input feature $X$ into $X_{1}$ and $X_{2}$ in the channel domain. The self-calibrated convolution first uses average pooling to reduce the input size and enlarge the receptive field:
\begin{align}
T_{1} = AvgPool_{r}(X_{1}) 
\end{align}
where $r$ is the filter size and stride. Then a convolution is used to model the channel relationship and a bilinear interpolation operator $Up$ is used to upsample the feature map: 

\begin{align}
X'_{1} = \text{Up}(Conv_2(T_1))
\end{align}

Next, element-wise multiplication finishes the self-calibrated process:

\begin{align}
Y'_{1} = Conv_3(X_1) \sigma(X_1 + X'_1)
\end{align}

Finally, the output feature map of is formed:
\begin{align}
Y_{1} &= Conv_4(Y'_{1})
\end{align}
\begin{align}
Y_2 &= Conv_1(X_2)
\end{align}
\begin{align}
Y &= [Y_1; Y_2]
\end{align}
Such self-calibrated convolution can enlarge the receptive field of a network and improve its adaptability. It achieves excellent results in image classification and certain downstream tasks such as instance segmentation, object detection and keypoint detection.

Source	DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com