**HS-ResNet** is a [convolutional neural network](https://paperswithcode.com/methods/category/convolutional-neural-networks) that employs [Hierarchical-Split Block](https://paperswithcode.com/method/hierarchical-split-block) as its central building block within a [ResNet](https://paperswithcode.com/method/resnet)-like architecture.

A **Hopfield Layer** is a module that enables a network to associate two sets of vectors. This general functionality allows for [transformer](https://paperswithcode.com/method/transformer)-like self-attention, for decoder-encoder attention, for time series prediction (maybe with positional encoding), for sequence analysis, for multiple instance learning, for learning with point sets, for combining data sources by associations, for constructing a memory, for averaging and pooling operations, and for many more. 

In particular, the Hopfield layer can readily be used as plug-in replacement for existing layers like pooling layers ([max-pooling](https://paperswithcode.com/method/max-pooling) or [average pooling](https://paperswithcode.com/method/average-pooling), permutation equivariant layers, [GRU](https://paperswithcode.com/method/gru) & [LSTM](https://paperswithcode.com/method/lstm) layers, and attention layers. The Hopfield layer is based on modern Hopfield networks with continuous states that have very high storage capacity and converge after one update.

Hopfield Layer

Hopfield Networks is All You Need

HS-ResNet

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

**JLA**, or **Joint Learning Architecture**, is an approach for multiple object tracking and trajectory forecasting. It jointly trains a tracking and trajectory forecasting model, and the trajectory forecasts are used for short-term motion estimates in lieu of linear motion prediction methods such as the Kalman filter. It uses a [FairMOT](https://paperswithcode.com/method/fairmot) model as the base model because this architecture already performs detection and tracking. A forecasting branch is added to the network and is trained end-to-end. [FairMOT](https://paperswithcode.com/method/fairmot) consist of a backbone network utilizing [Deep Layer Aggregation](https://www.paperswithcode.com/method/dla), an object detection head, and a reID head.

Source	HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com