# HOKEM: Human and Object Keypoint-based Extension Module for Human-Object Interaction Detection

Source: [HOKEM: Human and Object Keypoint-based Extension Module for Human-Object Interaction Detection](<https://www.sota2.com/research/paper/68fa6fdc1e209131cea47b38>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:59.657Z

Authors: Yoshiki Ito

Published: 2023-06-25T14:40:26.000Z

## Abstract

Human-object interaction (HOI) detection for capturing relationships between humans and objects is an important task in the semantic understanding of images. When processing human and object keypoints extracted from an image using a graph convolutional network (GCN) to detect HOI, it is crucial to extract appropriate object keypoints regardless of the object type and to design a GCN that accurately captures the spatial relationships between keypoints. This paper presents the human and object keypoint-based extension module (HOKEM) as an easy-to-use extension module to improve the accuracy of the conventional detection models. The proposed object keypoint extraction method is simple yet accurately represents the shapes of various objects. Moreover, the proposed human-object adaptive GCN (HO-AGCN), which introduces adaptive graph optimization and attention mechanism, accurately captures the spatial relationships between keypoints. Experiments using the HOI dataset, V-COCO, showed that HOKEM boosted the accuracy of an appearance-based model by a large margin.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.14260>)

[PDF](<https://arxiv.org/pdf/2306.14260.pdf>)