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STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos

Almushyti, Muna; Li, Frederick W.B.

STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos Thumbnail


Authors

Muna Almushyti muna.i.almushyti@durham.ac.uk
PGR Student Doctor of Philosophy



Abstract

Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the SpatioTemporal Interaction Transformer-based (STIT) network to reason such changes. Specifically, spatial transformers learn humans and objects context at specific frame time. Temporal transformer then learns the relations at a higher level between spatial context representations at different time steps, capturing longterm dependencies across frames. We further investigate multiple hierarchy designs in learning human interactions. We achieved superior performance on Charades, Something-Something v1 and CAD-120 datasets, comparing to baseline models without learning human-object relations, or with prior graph-based networks. We also achieved state-of-the-art accuracy of 95.93% on CAD-120 dataset [1] by employing RGB data only.

Citation

Almushyti, M., & Li, F. W. (2022). STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos. . https://doi.org/10.1109/icpr56361.2022.9956030

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 26th International Conference on Pattern Recognition (ICPR)
Start Date Aug 21, 2022
End Date Aug 25, 2022
Acceptance Date May 17, 2022
Publication Date 2022-11
Deposit Date Oct 31, 2022
Publicly Available Date Nov 1, 2022
Publisher Institute of Electrical and Electronics Engineers
Pages 3287-3294
DOI https://doi.org/10.1109/icpr56361.2022.9956030
Public URL https://durham-repository.worktribe.com/output/1135752
Related Public URLs https://doi.org/10.1109/ICPR56361.2022.9956030
Additional Information 21-25 Aug. 2022

Files

Accepted Conference Proceeding (1.4 Mb)
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