Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2003.14111v2
• Date: Tue, 19 May 2020 07:04:42 GMT
• Title: Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
• Authors: Ziyu Liu, Hongwen Zhang, Zhenghao Chen, Zhiyong Wang, Wanli Ouyang
• Abstract summary: We propose a simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets.
• Score: 79.33539539956186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extractor. However, existing methods have limitations in achieving (1) unbiased long-range joint relationship modeling under multi-scale operators and (2) unobstructed cross-spacetime information flow for capturing complex spatial-temporal dependencies. In this work, we present (1) a simple method to disentangle multi-scale graph convolutions and (2) a unified spatial-temporal graph convolutional operator named G3D. The proposed multi-scale aggregation scheme disentangles the importance of nodes in different neighborhoods for effective long-range modeling. The proposed G3D module leverages dense cross-spacetime edges as skip connections for direct information propagation across the spatial-temporal graph. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.

Related papers

• Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition [0.0]
  In this paper, we propose self-attention GCN hybrid model, Multi-Scale Spatial-Temporal self-attention (MSST)-GCN. We utilize spatial self-attention module with adaptive topology to understand intra-frame interactions within a frame among different body parts, and temporal self-attention module to examine correlations between frames of a node.
  arXiv  Detail & Related papers  (2024-04-03T10:25:45Z)
• Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition [10.60209288486904]
  It's common for current methods in skeleton-based action recognition to mainly consider capturing long-term temporal dependencies. We propose a general framework, coined as STGAT, to model cross-spacetime information flow. STGAT achieves state-of-the-art performance on three large-scale datasets.
  arXiv  Detail & Related papers  (2022-08-18T02:34:46Z)
• Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition [13.15374205970988]
  We present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module. The MS-GC and MT-GC modules decompose the corresponding local graph convolution into a set of sub-graph convolutions, forming a hierarchical residual architecture. We propose a multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition.
  arXiv  Detail & Related papers  (2022-06-27T03:17:33Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition [62.46544616232238]
  Previous motion recognition methods have achieved promising performance through the tightly coupled multi-temporal representation. We propose to decouple and recouple caused caused representation for RGB-D-based motion recognition.
  arXiv  Detail & Related papers  (2021-12-16T18:59:47Z)
• Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition [49.163326827954656]
  We propose a novel multi-granular-temporal graph network for skeleton-based action classification. We develop a dual-head graph network consisting of two inter-leaved branches, which enables us to extract at least two-temporal resolutions. We conduct extensive experiments on three large-scale datasets.
  arXiv  Detail & Related papers  (2021-08-10T09:25:07Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Temporal Graph Modeling for Skeleton-based Action Recognition [25.788239844759246]
  We propose a Temporal Enhanced Graph Convolutional Network (TE-GCN) to capture complex temporal dynamic. The constructed temporal relation graph explicitly builds connections between semantically related temporal features. Experiments are performed on two widely used large-scale datasets.
  arXiv  Detail & Related papers  (2020-12-16T09:02:47Z)
• Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition [57.98278794950759]
  Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data. We present a novel spatial-temporal GCN architecture which is defined via the Poincar'e geometry. We evaluate our method on two current largest scale 3D datasets.
  arXiv  Detail & Related papers  (2020-07-30T18:23:18Z)
• Temporal Extension Module for Skeleton-Based Action Recognition [0.0]
  We present a module that extends the temporal graph of a graph convolutional network (GCN) for action recognition with a sequence of skeletons. Our module is a simple yet effective method to extract correlated features of multiple joints in human movement.
  arXiv  Detail & Related papers  (2020-03-19T18:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.