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Learning to Anticipate Future with Dynamic Context Removal
[article]
2022
arXiv
pre-print
Anticipating future events is an essential feature for intelligent systems and embodied AI. However, compared to the traditional recognition task, the uncertainty of future and reasoning ability requirement make the anticipation task very challenging and far beyond solved. In this filed, previous methods usually care more about the model architecture design or but few attention has been put on how to train an anticipation model with a proper learning policy. To this end, in this work, we
arXiv:2204.02587v2
fatcat:ne7uwvgztzhwrm4ojkd4wrbvua