Search Results for author:
Found 3 papers, 0 papers with code
Analysis over vision-based models for pedestrian action anticipation
Therefore, we provide insights on the explainability of vision-based Transformer models in the context of pedestrian action prediction.
PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer
Finally, we justify our usage of the parallel decoding technique by showing that the trajectory prediction task can be better solved as a non-autoregressive task.
Is attention to bounding boxes all you need for pedestrian action prediction?
Interestingly, we showed that pre-training our Transformer model on the CP2A dataset and then fine-tuning it on the PIE dataset is beneficial for the action prediction task.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
