Relational Context Learning for Human-Object Interaction Detection

Dandan Dong, Zhiyang Jia*, Hang Chen, Kang Ji

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Interaction action recognition is a popular direction in recent years, as a key technology for understanding human behavior, it plays a crucial role in many fields such as intelligent surveillance and human-computer interaction. Current research often adopts CNN-based or Transformer-based methods, but most of these methods suffer from the problem of lack of context exchange. Aiming at the above problems, the thesis designs and implements a relational context-based interaction action recognition model based on the multivariate relational network based on Transformer method. The main work is as follows: The paper obtains and verifies the runnability of the multivariate relational network (MUREN) source code and explores suitable attention mechanisms for comparison with the original model. First, a channel attention mechanism is used, which, when combined with the MUREN model, achieves a performance improvement of more than 3% on the V-COCO dataset. Tests were conducted on the HICO-DET dataset, which showed that the improved channel attention mechanism was ineffective. Then moving to the use of the global contextual attention mechanism, which matches the relational contextual properties of MUREN, V-COCO is tested only on the HICO-DET dataset since it has already been improved on the channel attention, and the experimental results show an improvement of about 4% in the overall accuracy, and an improvement of about 7% and 6% in the rare samples and the recall rate, respectively.

Original languageEnglish
Title of host publicationProceedings of 2024 8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024
PublisherAssociation for Computing Machinery, Inc
Pages11-15
Number of pages5
ISBN (Electronic)9798400710094
DOIs
Publication statusPublished - 20 Feb 2025
Externally publishedYes
Event8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024 - Haikou, China
Duration: 18 Oct 202420 Oct 2024

Publication series

NameProceedings of 2024 8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

Conference

Conference8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024
Country/TerritoryChina
CityHaikou
Period18/10/2420/10/24

Keywords

  • channel attention mechanism
  • global contextual attention mechanism
  • interaction action recognition
  • multivariate relational networks

Fingerprint

Dive into the research topics of 'Relational Context Learning for Human-Object Interaction Detection'. Together they form a unique fingerprint.

Cite this