A [GCN](https://paperswithcode.com/method/gcn) method targeted at the unique spectral properties of dense graphs and hypergraphs, enabled by efficient numerical linear algebra.

**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.

YOLOP

YOLOP: You Only Look Once for Panoptic Driving Perception

PinvGCN

Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs

**Demon CM**, or **SGD with Momentum and Demon**,  is the [Demon](https://paperswithcode.com/method/demon) momentum rule applied to [SGD with momentum](https://paperswithcode.com/method/sgd-with-momentum).

$$ \beta\_{t} = \beta\_{init}\cdot\frac{\left(1-\frac{t}{T}\right)}{\left(1-\beta\_{init}\right) + \beta\_{init}\left(1-\frac{t}{T}\right)} $$

$$ \theta\_{t+1} = \theta\_{t} - \eta{g}\_{t} + \beta\_{t}v\_{t} $$

$$ v\_{t+1} = \beta\_{t}{v\_{t}} - \eta{g\_{t}} $$

Source	Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com