Methods › Computer Vision › Trajectory Data Augmentation › SimAug

Simulation as Augmentation

SimAug

4 papers tagged archive 2025-07-28

Introduced by Junwei Liang et al. in SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SimAug, or Simulation as Augmentation, is a data augmentation method for trajectory prediction. It augments the representation such that it is robust to the variances in semantic scenes and camera views. First, to deal with the gap between real and synthetic semantic scene, it represents each training trajectory by high-level scene semantic segmentation features, and defends the model from adversarial examples generated by whitebox attack methods. Second, to overcome the changes in camera views, it generates multiple views for the same trajectory, and encourages the model to focus on the “hardest” view to which the model has learned. The classification loss is adopted and the view with the highest loss is favored during training. Finally, the augmented trajectory is computed as a convex combination of the trajectories generated in previous steps. The trajectory prediction model is built on a multi-scale representation and the final model is trained to minimize the empirical vicinal risk over the distribution of augmented trajectories.

PaperSource

Papers archive 2025-07-28

4 shown of 4, 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.

Tasks archive 2025-07-28

11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Trajectory Prediction3
Adversarial Attack2
Adversarial Defense2
Pedestrian Trajectory Prediction2
Trajectory Forecasting2
Action Detection1
Autonomous Driving1
Collaborative Filtering1
Data Augmentation1
Fairness1
Prediction1

Usage over time archive 2025-07-28

Papers per year tagged with SimAug: 2020 to 2025, peak 3 3 0 2020: 3 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

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

Trajectory Data AugmentationAdversarial Training

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