Article ; Online: MLMG-SGG: Multilabel Scene Graph Generation With Multigrained Features.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
2024 Volume 33, Page(s) 1549–1559
Abstract: 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 ... ...
Abstract | 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. |
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Language | English |
Publishing date | 2024-02-27 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 1941-0042 |
ISSN (online) | 1941-0042 |
DOI | 10.1109/TIP.2022.3199089 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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