The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Graph Structured Network for Image-Text Matching
Open accessAbstract
Image-text matching has received growing interest since it bridges vision and language. The key challenge lies in how to learn correspondence between image and text. Existing works learn coarse correspondence based on object co-occurrence statistics, while failing to learn fine-grained phrase correspondence. In this paper, we present a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence. The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase. This is achieved by node-level matching and structure-level matching. The node-level matching associates each node with its relevant nodes from another modality, where the node can be object, relation or attribute. The associated nodes then jointly infer fine-grained correspondence by fusing neighborhood associations at structure-level matching. Comprehensive experiments show that GSMN outperforms state-of-the-art methods on benchmarks, with relative Recall@1 improvements of nearly 7% and 2% on Flickr30K and MSCOCO, respectively. Code will be released at: https://github.com/CrossmodalGroup/GSMN.
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Multimodal learning with graphs
Ektefaie, Y., Dasoulas, G., Noori, A., Farhat, M., & Zitnik, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00624-6 · Cited by 166
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Gan, Z., Li, L., Li, C., Wang, L., Liu, Z., & Gao, J. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000105 · Cited by 155
AI-assisted automated scoring of picture-cued writing tasks for language assessment
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Metadata
- Authors
- Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., & Zhang, Y.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10918-10927
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035605030
- Cited by
- 285
- References
- 38
- FWCI
- 12.17
- Type
- conference paper
- Language
- en
- Open access
- green
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