Object-Relation Reasoning Graph for Action Recognition
Yangjun Ou, Li Mi, Zhenzhong Chen
Abstract
Action recognition is a challenging task since the attributes of objects as well as their relationships change constantly in the video. Existing methods mainly use object-level graphs or scene graphs to represent the dynamics of objects and relationships, but ignore modeling the fine-grained relationship transitions directly. In this paper, we propose an Object-Relation Reasoning Graph (OR2G) for reasoning about action in videos. By combining an object-level graph (OG) and a relation-level graph (RG), the proposed OR2G catches the attribute transitions of objects and reasons about the relationship transitions between objects simultaneously. In addition, a graph aggregating module (GAM) is investigated by applying the multi-head edge-to-node message passing operation. GAM feeds back the information from the relation node to the object node and enhances the coupling between the object-level graph and the relation-level graph. Experiments in video action recognition demonstrate the effectiveness of our approach when compared with the state-of-the-art methods.
BibTeX
@inproceedings{cvpr2022_objectrelationre,
title = {Object-Relation Reasoning Graph for Action Recognition},
author = {Yangjun Ou and Li Mi and Zhenzhong Chen},
booktitle = {CVPR 2022},
year = {2022}
}