这篇博客是关于2020年6月第一周的多模态文献阅读报告，涉及Tensor Fusion Network在多模态情感分析中的应用，不完全时间序列数据的低秩表示学习，以及跨模态注意力机制和循环翻译方法在不规则数据上的表现。论文主要关注多模态融合、低秩张量表示、数据不完整性和跨模态注意力交互等关键问题，并在CMU-MOSI等数据集上进行了实验，证明了所提出方法的有效性。
Week Literature Reading Report
2020/06/08-2020/06/12 week 1
Paper 1
Tensor Fusion Network for Multimodal Sentiment Analysis
Issues that need resolving
Multimodal fusion and representation
Key insight
This paper use three-fold Cartesian product to fusion three modalities, which is called Tensor Fusion Network. This 3D tensor contains unimodal, bimodal and trimodal dynamics. Then put this tensor to a fully connected deep neural network to do prediction. Tensor not only can keep intra-modality information, but also learns inter-modality information.
The net structure
Dataset and experiment resul
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