May 9, 2017 · We theoretically show that STMN recasts the problem as projection over the manifold via an embedding method. The proposed approach is evaluated ...

This work proposes leveraging the manifold structure to constrain the deep action feature learning, thereby minimizing the intra-class variations in the ...

We theoretically show that STMN recasts the problem as projection over the manifold via an embedding method. The proposed approach is evaluated on two benchmark ...

May 9, 2017 · Abstract. Visual data such as videos are often sampled from complex manifold. We propose leveraging the manifold structure to constrain the ...

May 12, 2017 · We theoretically show that STMN recasts the problem as projection over the manifold via an embedding method. The proposed approach is evaluated ...

We propose leveraging the manifold structure to constrain the deep action ... Deep Spatio-temporal Manifold Network for Action Recognition. 2017·arXiv. Paper.

this paper, a spatio-temporal manifold network which devises manifold structure in a deep neural network architecture for action recognition is proposed.

Apr 25, 2019 · In this paper, we introduce a new spatio-temporal manifold network (STMN) that leverages data manifold structures to regularize deep action ...

Missing: Manifold | Show results with:Manifold

Therefore, in this paper, we propose a novel idea of action embedding with a self-attention Transformer network for skeleton-based action recognition. Our ...