The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Visual Semantic Reasoning for Image-Text Matching
Open accessAbstract
Image-text matching has been a hot research topic bridging the vision and language areas. It remains challenging because the current representation of image usually lacks global semantic concepts as in its corresponding text caption. To address this issue, we propose a simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene. Specifically, we first build up connections between image regions and perform reasoning with Graph Convolutional Networks to generate features with semantic relationships. Then, we propose to use the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced features, select the discriminative information and gradually generate the representation for the whole scene. Experiments validate that our method achieves a new state-of-the-art for the image-text matching on MS-COCO and Flickr30K datasets. It outperforms the current best method by 6.8% relatively for image retrieval and 4.8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set). On Flickr30K, our model improves image retrieval by 12.6% relatively and caption retrieval by 5.8% relatively (Recall@1).
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Cites
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Microsoft COCO: Common Objects in Context
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Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., & Duerig, T. (2021) · arXiv · doi:10.48550/arxiv.2102.05918 · Cited by 1,977
“R@1 R@5 R@10 R@1 R@5 R@10 R@1 R@5 R@10 R@1 R@5 R@10 Zero-shot (Faghri et al., 2018), VSRN (Li et al., 2019), DE I2T (Parekh et al., 2021), and DE T2T+I2T (Parekh et al., 2021). image R@1 R@5 R@10 R@1 R@5 R@10 R@1 R@5 R@10 R@1 R@5 R@10 VSE++ ERNIE-ViL (Yu et al., 2020), VILLA (Gan et al., 2020) and Oscar (Li et al., 20…”
BackgroundMethodInfluentialAlign before Fuse: Vision and Language Representation Learning with Momentum Distillation
Li, J., Selvaraju, R. R., Gotmare, A. D., Joty, S., Xiong, C., & Hoi, S. (2021) · arXiv · doi:10.48550/arxiv.2107.07651 · Cited by 1,078
Huang, S.-C., Shen, L., Lungren, M. P., & Yeung, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00391 · Cited by 489
IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Chen, H., Ding, G., Liu, X., Lin, Z., Liu, J., & Han, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01267 · Cited by 429
Similarity Reasoning and Filtration for Image-Text Matching
Diao, H., Zhang, Y., Ma, L., & Lu, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16209 · Cited by 361
Self-citation
Metadata
- Authors
- Li, K., Zhang, Y., Li, K., Li, Y., & Fu, Y.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 2019
- Pages
- 4653-4661
- OpenAlex
- W2988823324
- Cited by
- 599
- References
- 44
- FWCI
- 20.32
- Type
- conference paper
- Language
- en
- Open access
- green
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