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    Accepted author manuscript, 3.69 MB, PDF document

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IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Standard

IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition. / Rahmani, Hossein.
European Conference on Computer Vision (ECCV). Springer, 2022.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Rahmani, H 2022, IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition. in European Conference on Computer Vision (ECCV). Springer. <https://arxiv.org/abs/2207.12100>

APA

Rahmani, H. (2022). IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition. In European Conference on Computer Vision (ECCV) Springer. Advance online publication. https://arxiv.org/abs/2207.12100

Vancouver

Rahmani H. IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition. In European Conference on Computer Vision (ECCV). Springer. 2022 Epub 2022 Oct 27.

Author

Rahmani, Hossein. / IGFormer : Interaction Graph Transformer for Skeleton-based Human Interaction Recognition. European Conference on Computer Vision (ECCV). Springer, 2022.

Bibtex

@inproceedings{d647e357fa31486c9976f7b9e7406659,
title = "IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition",
abstract = "Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFormer constructs interaction graphs according to the semantic and distance correlations between the interactive body parts,and enhances the representation of each person by aggregating the informationof the interactive body parts based on the learned graphs. Furthermore, we propose a Semantic Partition Module to transform each human skeleton sequence into a Body-Part-Time sequence to better capture the spatial and temporal information of the skeleton sequence for learning the graphs. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art with a significant margin.",
author = "Hossein Rahmani",
year = "2022",
month = oct,
day = "27",
language = "English",
booktitle = "European Conference on Computer Vision (ECCV)",
publisher = "Springer",

}

RIS

TY - GEN

T1 - IGFormer

T2 - Interaction Graph Transformer for Skeleton-based Human Interaction Recognition

AU - Rahmani, Hossein

PY - 2022/10/27

Y1 - 2022/10/27

N2 - Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFormer constructs interaction graphs according to the semantic and distance correlations between the interactive body parts,and enhances the representation of each person by aggregating the informationof the interactive body parts based on the learned graphs. Furthermore, we propose a Semantic Partition Module to transform each human skeleton sequence into a Body-Part-Time sequence to better capture the spatial and temporal information of the skeleton sequence for learning the graphs. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art with a significant margin.

AB - Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFormer constructs interaction graphs according to the semantic and distance correlations between the interactive body parts,and enhances the representation of each person by aggregating the informationof the interactive body parts based on the learned graphs. Furthermore, we propose a Semantic Partition Module to transform each human skeleton sequence into a Body-Part-Time sequence to better capture the spatial and temporal information of the skeleton sequence for learning the graphs. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art with a significant margin.

M3 - Conference contribution/Paper

BT - European Conference on Computer Vision (ECCV)

PB - Springer

ER -