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• W4312777907 abstract "Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the Spatio-Temporal 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 long-term 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." @default.
• W4312777907 created "2023-01-05" @default.
• W4312777907 creator A5001814342 @default.
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• W4312777907 date "2022-08-21" @default.
• W4312777907 modified "2023-09-27" @default.
• W4312777907 title "STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos" @default.
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• W4312777907 doi "https://doi.org/10.1109/icpr56361.2022.9956030" @default.
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