**TradeS** is an online joint detection and tracking model, coined as TRACK to DEtect and Segment, exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation.

**3DSSD** is a point-based 3D single stage object detection detector. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. The authors propose a fusion sampling strategy in the downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet the needs of accuracy and speed.

3DSSD

3DSSD: Point-based 3D Single Stage Object Detector

TraDeS

Track to Detect and Segment: An Online Multi-Object Tracker

**Population Based Training**, or **PBT**, is an optimization method for finding parameters and hyperparameters, and extends upon parallel search methods and sequential optimisation methods.
It leverages information sharing across a population of concurrently running optimisation processes, and allows for online propagation/transfer of parameters and hyperparameters between members of the population based on their performance. Furthermore, unlike most other adaptation schemes, the method is capable of performing online adaptation of hyperparameters -- which can be particularly important in problems with highly non-stationary learning dynamics, such as reinforcement learning settings. PBT is decentralised and asynchronous, although it could also be executed semi-serially or with partial synchrony if there is a binding budget constraint.

Source	Track to Detect and Segment: An Online Multi-Object Tracker
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com