Visual Relationship Detection with Multimodal Fusion and Reasoning Article Swipe
Visual relationship detection aims to completely understand visual scenes and has recently received increasing attention. However, current methods only use the visual features of images to train the semantic network, which does not match human habits in which we know obvious features of scenes and infer covert states using common sense. Therefore, these methods cannot predict some hidden relationships of object-pairs from complex scenes. To address this problem, we propose unifying vision–language fusion and knowledge graph reasoning to combine visual feature embedding with external common sense knowledge to determine the visual relationships of objects. In addition, before training the relationship detection network, we devise an object–pair proposal module to solve the combination explosion problem. Extensive experiments show that our proposed method outperforms the state-of-the-art methods on the Visual Genome and Visual Relationship Detection datasets.
Related Topics
Concepts
Computer science
Artificial intelligence
Visual reasoning
Covert
Scene graph
Embedding
Visualization
Object (grammar)
Feature (linguistics)
Object detection
Graph
Machine learning
Natural language processing
Pattern recognition (psychology)
Theoretical computer science
Rendering (computer graphics)
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22207918
- https://www.mdpi.com/1424-8220/22/20/7918/pdf?version=1666080963
- OA Status
- gold
- Cited By
- 4
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306770008
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306770008Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22207918Digital Object Identifier
- Title
-
Visual Relationship Detection with Multimodal Fusion and ReasoningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-18Full publication date if available
- Authors
-
Shouguan Xiao, Weiping FuList of authors in order
- Landing page
-
https://doi.org/10.3390/s22207918Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/20/7918/pdf?version=1666080963Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/20/7918/pdf?version=1666080963Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Visual reasoning, Covert, Scene graph, Embedding, Visualization, Object (grammar), Feature (linguistics), Object detection, Graph, Machine learning, Natural language processing, Pattern recognition (psychology), Theoretical computer science, Rendering (computer graphics), Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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