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arXiv 2109.09020cs.LGcs.AI
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多模式分类:现状、分类学和未来方向

Multimodal Classification: Current Landscape, Taxonomy and Future Directions

Virginia Commonwealth University(弗吉尼亚联邦大学)

William C. Sleeman, Rishabh Kapoor, Preetam Ghosh

摘要:

多模态分类研究在许多领域越来越受欢迎,这些领域从多个来源收集更多数据,包括卫星图像,生物识别和医学。但是,由于缺乏一致的术语和体系结构描述,很难比较不同的现有解决方案。我们解决这些挑战,提出了一个新的分类方法来描述这种系统的基础上发现的趋势,在最近的出版物多模态分类。单峰分类的许多最困难的方面尚未完全解决多模态数据集,包括大数据,类不平衡和实例级难度。我们还讨论了这些挑战和未来的发展方向。

英文摘要:

Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine. However, the lack of consistent terminology and architectural descriptions makes it difficult to compare different existing solutions. We address these challenges by proposing a new taxonomy for describing such systems based on trends found in recent publications on multimodal classification. Many of the most difficult aspects of unimodal classification have not yet been fully addressed for multimodal datasets including big data, class imbalance, and instance level difficulty. We also provide a discussion of these challenges and future directions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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