The KITTI Vision Benchmark Suite

This paper proposes a self-supervised objective for learning
representations that support localization of objects under occlusion -
a property

known as object permanence. Instead of directly supervising
locations of invisible objects, we show that object permanence can
emerge by

optimizing for a Markov random walk along a space-time graph of
memories, provided that the states in each time step are (non-
Markov)

features from a recurrent encoder.

Will be made available with the manuscript.

@inproceedings{tokmakov2022object,

title={Object Permanence Emerges in a Random Walk along
Memory},

author={Tokmakov, Pavel and Jabri, Allan and Li, Jie and Gaidon,
Adrien},

booktitle={ICML},

year={2022}

}