Abstract | ||
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Trajectory forecasting and imputation are pivotal steps towards understanding the movement of human and objects, which are quite challenging since the future trajectories and missing values in a temporal sequence are full of uncertainties, and the spatial-temporally contextual correlation is hard to model. Yet, the relevance between sequence prediction and imputation is disregarded by existing approaches. To this end, we propose a novel imitative non-autoregressive modeling method to simultaneously handle the trajectory prediction task and the missing value imputation task. Specifically, our framework adopts an imitation learning paradigm, which contains a recurrent conditional variational autoencoder (RC-VAE) as a demonstrator, and a non-autoregressive transformation model (NART) as a learner. By jointly optimizing the two models, RC-VAE can predict the future trajectory and capture the temporal relationship in the sequence to supervise the NART learner. As a result, NART learns from the demonstrator and imputes the missing value in a non autoregressive strategy. We conduct extensive experiments on three popular datasets, and the results show that our model achieves state-of-the-art performance across all the datasets. |
Year | DOI | Venue |
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2020 | 10.1109/CVPR42600.2020.01275 | CVPR |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
29 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mengshi Qi | 1 | 36 | 3.91 |
Jie Qin | 2 | 167 | 17.38 |
Yu Wu | 3 | 192 | 12.13 |
Yi Yang | 4 | 6873 | 271.72 |