Methods › Computer Vision › Multi-Object Tracking Models › JLA
Joint Learning Architecture
JLA
Introduced by Oluwafunmilola Kesa et al. in Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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 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 consist of a backbone network utilizing Deep Layer Aggregation, an object detection head, and a reID head.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting 24 Aug 2021 · 1 repository · arXiv:2108.10543
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Multiple Object Tracking | 1 |
| Object Tracking | 1 |
| Prediction | 1 |
| Trajectory Forecasting | 1 |
| motion prediction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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