The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
Open accessAbstract
Understanding the visual relationship between two objects involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the hsubj; obji pair (both semantically and spatially) to predict predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships compared to modeling them independently, but it complicates learning since the semantic space of visual relationships is huge and training data is limited, especially for longtail relationships that have few instances. To overcome this, we use knowledge of linguistic statistics to regularize visual model learning. We obtain linguistic knowledge by mining from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge), computing the conditional probability distribution of a predicate given a (subj, obj) pair. As we train the visual model, we distill this knowledge into the deep model to achieve better generalization. Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the stateof- the-art methods significantly, especially when predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on VRD zero-shot testing set).
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Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
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Introduction to Information Retrieval
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Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Context based object categorization: A critical survey
Galleguillos, C., & Belongie, S. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.02.004 · Cited by 351
Cited by
Improved Knowledge Distillation via Teacher Assistant
Mirzadeh, S. I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., & Ghasemzadeh, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5963 · Cited by 101
“To solve this issue, Yim et al.; Yu et al. (2017; 2017) used a shared representation of layers, however, it’s not straight-forward to choose the appropriate layer to be matched.”
BackgroundSelf-citationNeural 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
Revisiting Knowledge Distillation via Label Smoothing Regularization
Yuan, L., Tay, F. E., Li, G., Wang, T., & Feng, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00396 · Cited by 572
Knowledge Distillation via Instance Relationship Graph
Liu, Y., Cao, J., Li, B., Yuan, C., Hu, W., Li, Y., & Duan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00726 · 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
- Yu, R., Li, A., Morariu, V. I., & Davis, L. S.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1068-1076
- OpenAlex
- W2962737704
- Cited by
- 310
- References
- 7
- FWCI
- 11.24
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yu, R., Li, A., Morariu, V. I., & Davis, L. S. (2017). Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.121
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