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Found 5 papers, 0 papers with code
Action Recognition with Deep Multiple Aggregation Networks
While convolutional and fully connected operations have been widely studied in the literature, the design of pooling operations that handle action recognition, with different sources of temporal granularity in action categories, has comparatively received less attention, and existing solutions rely mainly on max or averaging operations.
Deep hierarchical pooling design for cross-granularity action recognition
In this paper, we introduce a novel hierarchical aggregation design that captures different levels of temporal granularity in action recognition.
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks
The main contribution of this method resides in a new design principle that learns graph-laplacians as convex combinations of other elementary laplacians each one dedicated to a particular topology of the input graphs.
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition
We also introduce a novel pooling operator, on graphs, that proceeds in two steps: context-dependent node expansion is achieved, followed by a global average pooling; the strength of this two-step process resides in its ability to preserve the discrimination power of nodes while achieving permutation invariance.
Ranked #2 on
Skeleton Based Action Recognition
on SBU
Human Action Recognition with Deep Temporal Pyramids
Our solution is based on a tree-structured temporal pyramid that aggregates outputs of CNNs at different levels.
