The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Open accessAbstract
Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner. However, most of them consider all semantics equally and thus align them uniformly, regardless of their diverse complexities. In fact, semantics are diverse (i.e. involving different kinds of semantic concepts), and humans usually follow a latent structure to combine them into understandable languages. It may be difficult to optimally capture such sophisticated correspondences in existing methods. In this paper, to address such a deficiency, we propose an Iterative Matching with Recurrent Attention Memory (IMRAM) method, in which correspondences between images and texts are captured with multiple steps of alignments. Specifically, we introduce an iterative matching scheme to explore such fine-grained correspondence progressively. A memory distillation unit is used to refine alignment knowledge from early steps to later ones. Experiment results on three benchmark datasets, i.e. Flickr8K, Flickr30K, and MS COCO, show that our IMRAM achieves state-of-the-art performance, well demonstrating its effectiveness. Experiments on a practical business advertisement dataset, named KWAI-AD, further validates the applicability of our method in practical scenarios.
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Cited by
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
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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
Towards artificial general intelligence via a multimodal foundation model
Fei, N., Lu, Z., Gao, Y., Yang, G., Huo, Y., Wen, J., Lu, H., Song, R., Gao, X., Xiang, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30761-2 · Cited by 280
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
Bain, M., Nagrani, A., Varol, G., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00175 · Cited by 76
AI-assisted automated scoring of picture-cued writing tasks for language assessment
Zhao, R., Zhuang, Y., Zou, D., Xie, Q., & Yu, P. L. H. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11473-y · Cited by 40
Metadata
- Authors
- Chen, H., Ding, G., Liu, X., Lin, Z., Liu, J., & Han, J.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12652-12660
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035454331
- Cited by
- 429
- References
- 30
- FWCI
- 17.44
- Type
- conference paper
- Language
- en
- Open access
- green
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- Chen, H., Ding, G., Liu, X., Lin, Z., Liu, J., & Han, J. (2020). IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.01267
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