Spatial CNN with UNet based Encoder-decoder and ConvLSTM

**GBST**, or **Gradient-based Subword Tokenization Module**, is a soft gradient-based subword tokenization module that automatically learns latent subword representations from characters in a data-driven fashion. Concretely, GBST enumerates candidate subword blocks and learns to score them in a position-wise fashion using a block scoring network.  

GBST learns a position-wise soft selection over candidate subword blocks by scoring them with a scoring network. In contrast to prior tokenization-free methods, GBST learns interpretable latent subwords, which enables easy inspection of lexical representations and is more efficient than other byte-based models.

Gradient-Based Subword Tokenization

Charformer: Fast Character Transformers via Gradient-based Subword Tokenization

SCNN_UNet_ConvLSTM

A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection

Decoder architecture inspired on the [UNet++](https://paperswithcode.com/method/unet) structure and the [EfficientNet](https://paperswithcode.com/method/efficientnet) building blocks. Keeping the UNet++ structure, the EfficientUNet++ achieves higher performance and significantly lower computational complexity through two simple modifications:

* Replaces the 3x3 convolutions of the UNet++ with residual bottleneck blocks with depthwise convolutions
* Applies channel and spatial attention to the bottleneck feature maps using [concurrent spatial and channel squeeze & excitation (scSE)](https://paperswithcode.com/method/scse) blocks

Source	A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection
Year	2000
Data Source	Papers with Code archive (CC BY-SA)