MLMG-SGG: Multilabel Scene Graph Generation With Multigrained Features
As an important and challenging problem in computer vision, scene graph generation (SGG) aims to find out the underlying semantic relationships among objects from a given image for scene understanding. Usually, prevalent SGG approaches adopt a learning pipeline with the assumption that there exists...
Saved in:
Published in: | IEEE transactions on image processing 2024-01, Vol.33, p.1549-1559 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | As an important and challenging problem in computer vision, scene graph generation (SGG) aims to find out the underlying semantic relationships among objects from a given image for scene understanding. Usually, prevalent SGG approaches adopt a learning pipeline with the assumption that there exists only a single relationship for a particular object pair. Considering the common phenomenon that a pair of objects can be attached by multiple relationships, we propose a multi-label scene graph generation pipeline with multi-grained features (MLMG-SGG), which formulates the relationship detection as a multi-label classification problem during training while generating multigraphs at inference time. In order to better model the fine-grained relationships, the proposed pipeline encodes the feature representation of SGG on different spatial scales by a specially designed Multi-Grained Module (MGM), resulting in the multi-grained (i.e., object-level and region-level) features of objects. Experimental results over the benchmark dataset demonstrate the significant performance gain of the proposed pipeline used as a plug-in for the state-of-the-art methods. |
---|---|
ISSN: | 1057-7149 1941-0042 |
DOI: | 10.1109/TIP.2022.3199089 |