Feedback Graph Convolutional Network for Skeleton-based Action
Recognition
- URL: http://arxiv.org/abs/2003.07564v1
- Date: Tue, 17 Mar 2020 07:20:47 GMT
- Title: Feedback Graph Convolutional Network for Skeleton-based Action
Recognition
- Authors: Hao Yang, Dan Yan, Li Zhang, Dong Li, YunDa Sun, ShaoDi You, Stephen
J. Maybank
- Abstract summary: We propose a novel network, named Feedback Graph Convolutional Network (FGCN)
This is the first work that introduces the feedback mechanism into GCNs and action recognition.
It achieves the state-of-the-art performance on three datasets.
- Score: 38.782491442635205
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Skeleton-based action recognition has attracted considerable attention in
computer vision since skeleton data is more robust to the dynamic circumstance
and complicated background than other modalities. Recently, many researchers
have used the Graph Convolutional Network (GCN) to model spatial-temporal
features of skeleton sequences by an end-to-end optimization. However,
conventional GCNs are feedforward networks which are impossible for low-level
layers to access semantic information in the high-level layers. In this paper,
we propose a novel network, named Feedback Graph Convolutional Network (FGCN).
This is the first work that introduces the feedback mechanism into GCNs and
action recognition. Compared with conventional GCNs, FGCN has the following
advantages: (1) a multi-stage temporal sampling strategy is designed to extract
spatial-temporal features for action recognition in a coarse-to-fine
progressive process; (2) A dense connections based Feedback Graph Convolutional
Block (FGCB) is proposed to introduce feedback connections into the GCNs. It
transmits the high-level semantic features to the low-level layers and flows
temporal information stage by stage to progressively model global
spatial-temporal features for action recognition; (3) The FGCN model provides
early predictions. In the early stages, the model receives partial information
about actions. Naturally, its predictions are relatively coarse. The coarse
predictions are treated as the prior to guide the feature learning of later
stages for a accurate prediction. Extensive experiments on the datasets,
NTU-RGB+D, NTU-RGB+D120 and Northwestern-UCLA, demonstrate that the proposed
FGCN is effective for action recognition. It achieves the state-of-the-art
performance on the three datasets.
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