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SHERLock: Self-Supervised Hierarchical Event Representation Learning. (arXiv:2010.02556v2 [cs.LG] UPDATED)
cs.CL updates on arXiv.org arxiv.org
Temporal event representations are an essential aspect of learning among
humans. They allow for succinct encoding of the experiences we have through a
variety of sensory inputs. Also, they are believed to be arranged
hierarchically, allowing for an efficient representation of complex
long-horizon experiences. Additionally, these representations are acquired in a
self-supervised manner. Analogously, here we propose a model that learns
temporal representations from long-horizon visual demonstration data and
associated textual descriptions, without explicit temporal supervision. Our
method produces a …
arxiv event hierarchical learning lg representation representation learning sherlock