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MLMG-SGG: Multilabel Scene Graph Generation With Multigrained Features
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AI
DOI：10.1109/TIP.2022.3199089.png)
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摘要
En 中文
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.
Keyword:
Pipelines
Feature extraction
Detectors
Image edge detection
Task analysis
Visualization
Object detection
Scene graph generation
multi-grained
multi-label classification
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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