Work

Relational Context Learning for Human-Object Interaction Detection

Year: 2024

Type: conference-paper

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:

Source: International Conference on Electronic Information Technology and Computer Engineering (EITCE)

DOI: 10.1145/3711129.3711132

Authors: Dandan Dong, Zhiyang Jia, Hang Chen, Kang Ji

Institutions: China University of Petroleum, Beijing, PetroChina Xinjiang Oilfield Company (China)

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Natural Science Foundation of Xinjiang

OpenAlex ID: W4407774577

FWCI:

Cites: 13

Cited by:

Related to: 10