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Updated: Jul 10, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Human-Object Interaction detection via Global Context and Pairwise-level Fusion Features Integration.

Haozhong Wang¹, Hua Yu¹, Qiang Zhang¹

¹Dalian University of Technology, Dalian, 116024, Liaoning, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 23, 2023

Summary

This summary is machine-generated.

This study introduces the Global Context and Pairwise-level Fusion Features Integration Network (GFIN) for improved Human-Object Interaction (HOI) detection. GFIN enhances HOI recognition by focusing on interaction pairs and global context, outperforming existing methods.

Keywords:

Global context Human–object interaction Pairwise-level attention Two-stage detector

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Two-stage detector methods using transformers excel in Human-Object Interaction (HOI) detection.
Current methods lack focus on human-object interaction pair semantics and feature fusion for HOI recognition.

Purpose of the Study:

To propose a novel two-stage network, GFIN, for enhanced HOI detection.
To improve HOI recognition by integrating global context and pairwise interaction features.

Main Methods:

GFIN employs an object detector for initial instance feature extraction.
Key modules include Global Contextual Feature Extraction Encoder (GCE), Pairwise Interaction Query Decoder (PID), and Human-Object Pairwise-level Attention Fusion Module (HOF).
GCE integrates global context memory with local instance memory; HOF fuses multi-layer features using pairwise-level attention; PID decodes interaction recognition results.

Main Results:

GFIN significantly outperforms state-of-the-art methods on HICO-DET and V-COCO datasets.
The proposed pairwise-level attention mechanism effectively fuses and enhances interaction pair features.

Conclusions:

GFIN offers a superior approach to HOI detection by effectively leveraging global context and pairwise interaction information.
The network architecture demonstrates significant advancements in HOI recognition accuracy.