Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction

Zhan Su

doi:10.9781/ijimai.2023.06.004

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction

2025-01-01

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DOI：10.9781/ijimai.2023.06.004

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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 mAPro,e, which demonstrates the effectiveness of our proposed multi-level information detection strategy.

Keywords:

Computer Vision

Deep Learning

Graph Neural Network

HOI Detection

Image Understanding

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction

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