The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges
Open accessAbstract
Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly focused on single-media retrieval. However, the requirements of users are highly flexible, such as retrieving the relevant audio clips with one query of image. So challenges stemming from the “media gap,” which means that representations of different media types are inconsistent, have attracted increasing attention. Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types. As a relatively new research topic, its concepts, methodologies, and benchmarks are still not clear in the literature. To address these issues, we review more than 100 references, give an overview including the concepts, methodologies, major challenges, and open issues, as well as build up the benchmarks, including data sets and experimental results. Researchers can directly adopt the benchmarks to promptly evaluate their proposed methods. This will help them to focus on algorithm design, rather than the time-consuming compared methods and results. It is noted that we have constructed a new data setXMedia, which is the first publicly available data set with up to five media types (text, image, video, audio, and 3-D model). We believe this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Labaca-Castro, R. (2023) · Machine Learning under Malware Attack · doi:10.1007/978-3-658-40442-0_9 · Cited by 19,272
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,223
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318
RELATIONS BETWEEN TWO SETS OF VARIATES
HOTELLING, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 5,263
Canonical Correlation Analysis: An Overview with Application to Learning Methods
Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004) · Neural Computation · doi:10.1162/0899766042321814 · Cited by 3,202
(2011) · Cited by 2,752
Cited by
Deep Supervised Cross-Modal Retrieval
Zhen, L., Hu, P., Wang, X., & Peng, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01064 · Cited by 478
Scalable Deep Multimodal Learning for Cross-Modal Retrieval
Hu, P., Zhen, L., Peng, D., & Liu, P. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331213 · Cited by 136
Multi-pathway generative adversarial hashing for unsupervised cross-modal retrieval
Zhang, J., & Peng, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2922128 · Cited by 103
Self-citationJoint feature synthesis and embedding: Adversarial cross-modal retrieval revisited
Xu, X., Lin, K., Yang, Y., Hanjalic, A., & Shen, H. T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045530 · Cited by 88
Metadata
- Authors
- Peng, Y., Huang, X., & Zhao, Y.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2018-09
- Volume
- 28
- Issue
- 9
- Pages
- 2372-2385
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2606965845
- Cited by
- 341
- References
- 93
- FWCI
- 16.06
- Type
- journal article
- Language
- en
- Open access
- green
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