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.

**Flow Alignment Module**, or **FAM**, is a flow-based align module for scene parsing to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high resolution features effectively and efficiently. The concept of Semantic Flow is inspired from optical flow, which is widely used in video processing task to represent the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by relative motion. The authors postulate that the relatinship between two feature maps of arbitrary resolutions from the same image can also be represented with the “motion” of every pixel from one feature map to the other one. Once precise Semantic Flow is obtained, the network is able to propagate semantic features with minimal information loss.

In the FAM module, the transformed high-resolution feature map are combined with the low-resolution feature map to generate the semantic flow field, which is utilized to warp the low-resolution feature map to high-resolution feature map.

Flow Alignment Module

Semantic Flow for Fast and Accurate Scene Parsing

CharacterBERT

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

**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.

Source	CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com