The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Detecting Visual Relationships with Deep Relational Networks
Open accessAbstract
Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. ride) or each distinct visual phrase (e.g. person-ride-horse) as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships. On two large data sets, the proposed method achieves substantial improvement over state-of-the-art.
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Cites
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“In [51], the message passing among pixels is approximately instantiated using CNN filters and this is primarily suited for grid structures; while in DR-Net, the inference steps are exactly reproduced using fully-connected layers.”
BackgroundMethodInfluentialFlickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
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Graph convolutional networks: a comprehensive review
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Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00611 · Cited by 1,052
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
Chen, Y., Tai, L., Sun, K., & Li, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01211 · Cited by 316
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs
Ji, J., Krishna, R., Fei-Fei, L., & Niebles, J. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01025 · Cited by 303
Multi-Modal Knowledge Graph Construction and Application: A Survey
Zhu, X., Li, Z., Wang, X., Jiang, X., Sun, P., Wang, X., Xiao, Y., & Yuan, N. J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3224228 · Cited by 291
Metadata
- Authors
- Dai, B., Zhang, Y., & Lin, D.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3298-3308
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2607855566
- Cited by
- 502
- References
- 10
- FWCI
- 19.41
- Type
- conference paper
- Language
- en
- Open access
- green
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