Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.

中文翻译：

---

多模态机器学习：调查和分类


我们对世界的体验是多模式的——我们看到物体、听到声音、感觉到质地、闻到气味、尝到味道。模态是指某件事发生或经历的方式，当一个研究问题包含多种这样的模态时，它就被描述为多模态。为了让人工智能在理解我们周围的世界方面取得进展，它需要能够共同解释这些多模态信号。多模态机器学习旨在构建可以处理和关联来自多种模态的信息的模型。这是一个充满活力的多学科领域，其重要性与日俱增，并具有非凡的潜力。本文没有关注特定的多模态应用，而是调查了多模态机器学习本身的最新进展，并以通用分类法呈现它们。我们超越了典型的早期和晚期融合分类，并确定了多模式机器学习面临的更广泛的挑战，即：表示、翻译、对齐、融合和共同学习。这种新的分类法将使研究人员能够更好地了解该领域的现状并确定未来研究的方向。