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Visualization of T-MAE masking (middle) and reconstruction (right) for two ground truth multiagent trajectory instances (left). The reconstruction to the right is a combination of ground truth timesteps provided to the model (i.e. dots in the middle column with lS=5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_S = 5$$\end{document} and r=0.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r = 0.8$$\end{document}) and masked timesteps predicted by the model. The top/bottom row show a training/validation instance respectively

Visualization of T-MAE masking (middle) and reconstruction (right) for two ground truth multiagent trajectory instances (left). The reconstruction to the right is a combination of ground truth timesteps provided to the model (i.e. dots in the middle column with lS=5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_S = 5$$\end{document} and r=0.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r = 0.8$$\end{document}) and masked timesteps predicted by the model. The top/bottom row show a training/validation instance respectively

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Automatically labeling trajectories of multiple agents is key to behavioral analyses but usually requires a large amount of manual annotations. This also applies to the domain of team sport analyses. In this paper, we specifically show how pretraining transformer models improves the classification performance on tracking data from professional socc...

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