微笑与大笑检测的新视角：强度水平至关重要
A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter
AI总结：
本文提出基于深度学习的音频、视觉及融合多模态系统，将微笑与大笑作为不同表情分类，并分析强度水平与迁移学习对检测性能的影响。
摘要：
在过去十年中，微笑和大笑检测系统吸引了大量关注，并推动了人机交互系统的改进。但很少有研究将这些表情视为不同的实体，尽管此前没有工作明确证明它们是否属于同一类别。在本研究中，我们提出了一种基于深度学习的多模态微笑与大笑分类系统，并将二者视为两个不同的实体。我们比较了基于音频和基于视觉的模型，以及一种融合方法的使用。结果表明，正如预期的那样，融合方法在未见过的数据上带来了更好的泛化能力。我们还深入分析了这些模型在微笑和大笑强度水平上的行为。针对强度水平的分析表明，微笑与大笑之间的关系可能并不像二元关系那样简单，甚至也不应将它们归为单一类别；因此，在处理它们时应采用更复杂的方法。我们还解决了资源有限的问题，结果表明迁移学习能够使模型改进对易混淆强度水平的检测。
英文摘要：
Smiles and laughs detection systems have attracted a lot of attention in the past decade contributing to the improvement of human-agent interaction systems. But very few considered these expressions as distinct, although no prior work clearly proves them to belong to the same category or not. In this work, we present a deep learning-based multimodal smile and laugh classification system, considering them as two different entities. We compare the use of audio and vision-based models as well as a fusion approach. We show that, as expected, the fusion leads to a better generalization on unseen data. We also present an in-depth analysis of the behavior of these models on the smiles and laughs intensity levels. The analyses on the intensity levels show that the relationship between smiles and laughs might not be as simple as a binary one or even grouping them in a single category, and so, a more complex approach should be taken when dealing with them. We also tackle the problem of limited resources by showing that transfer learning allows the models to improve the detection of confusing intensity levels.
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。
