**Enhanced Sequential Inference Model** or **ESIM** is a sequential NLI model proposed in [Enhanced LSTM for Natural Language Inference](https://www.aclweb.org/anthology/P17-1152) paper.

A **CNN BiLSTM** is a hybrid bidirectional [LSTM](https://paperswithcode.com/method/lstm) and CNN architecture. In the original formulation applied to named entity recognition, it learns both character-level and word-level features. The CNN component is used to induce the character-level features. For each word the model employs a [convolution](https://paperswithcode.com/method/convolution) and a [max pooling](https://paperswithcode.com/method/max-pooling) layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.

CNN BiLSTM

Named Entity Recognition with Bidirectional LSTM-CNNs

ESIM

Enhanced LSTM for Natural Language Inference

**YOLOP** is a panoptic driving perception network for handling traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. It can be thought of a lightweight version of Tesla's HydraNet model for self-driving cars.

A lightweight CNN, from Scaled-yolov4, is used as the encoder to extract features from the image. Then these feature maps are fed to three decoders to complete their respective tasks. The detection decoder is based on the current best-performing single-stage detection network, [YOLOv4](https://paperswithcode.com/method/yolov4),  for two main reasons: (1) The single-stage detection network is faster than the two-stage detection network. (2) The grid-based prediction mechanism of the single-stage detector is more related to the other two semantic segmentation tasks, while instance segmentation is usually combined with the region based detector as in [Mask R-CNN](https://paperswithcode.com/method/mask-r-cnn). The feature map output by the encoder incorporates semantic features of different levels and scales, and our segmentation branch can use these feature maps to complete pixel-wise semantic prediction.

Source	Enhanced LSTM for Natural Language Inference
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com