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Deep hierarchical pooling design for cross-granularity action recognition
[article]
2020
arXiv
pre-print
In this paper, we introduce a novel hierarchical aggregation design that captures different levels of temporal granularity in action recognition. Our design principle is coarse-to-fine and achieved using a tree-structured network; as we traverse this network top-down, pooling operations are getting less invariant but timely more resolute and well localized. Learning the combination of operations in this network -- which best fits a given ground-truth -- is obtained by solving a constrained
arXiv:2006.04473v1
fatcat:wgn72n7jgbbgvewkjkwv7mwvwq