**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.

**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.

HS-ResNet

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

Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting

**Parrot** is an imitation learning approach to automatically learn cache access patterns by leveraging Belady’s optimal policy. Belady’s optimal policy is an oracle policy that computes the theoretically optimal cache eviction decision based on knowledge of future cache accesses, which Parrot approximates with a policy that only conditions on the past accesses.

Source	Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com