Causal Reasoning with Spatial-temporal Representation Learning: A Prospective Study. (arXiv:2204.12037v1 [cs.CV])

Spatial-temporal representation learning is ubiquitous in various real-world
applications, including visual comprehension, video understanding, multi-modal
analysis, human-computer interaction, and urban computing. Due to the emergence
of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal
data in big data era, the existing visual methods rely heavily on large-scale
data annotations and supervised learning to learn a powerful big model.
However, the lack of interpretability, robustness, and out-of-distribution
generalization are becoming the bottleneck problems of these models, which
hinders the progress of interpretable and …

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