Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction
Autor:
Su, Zhan
; Yu, Ruiyun
; Zou, Shihao
; Guo, Bingyang
; Cheng, Li
Fecha:
06/2023Palabra clave:
Revista / editorial:
International Journal of Interactive Multimedia and Artificial IntelligenceTipo de Ítem:
articleDirección web:
https://www.ijimai.org/journal/bibcite/reference/3334![Open Access Open Access](/themes/Mirage2/images/reunir/openaccess.png)
Resumen:
Human-Object Interaction (HOI) detection focuses on human-centered visual relationship detection, which is a challenging task due to the complexity and diversity of image content. Unlike most recent HOI detection works that only rely on paired instance-level information in the union range, our proposed Spatial-aware Multilevel Parsing Network (SMPNet) uses a multi-level information detection strategy, including instance-level visual features of detected human-object pair, part-level related features of the human body, and scene-level features extracted by the graph neural network. After fusing the three levels of features, the HOI relationship is predicted. We validate our method on two public datasets, V-COCO and HICO-DET. Compared with prior works, our proposed method achieves the state-of-the-art results on both datasets in terms of mAProle, which demonstrates the effectiveness of our proposed multi-level information detection strategy.
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