Summary of Toward Robust Multimodal Learning Using Multimodal Foundational Models, by Xianbing Zhao et al.
Toward Robust Multimodal Learning using Multimodal Foundational Models
by Xianbing Zhao, Soujanya Poria, Xuejiao Li, Yixin Chen, Buzhou Tang
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework called TRML (Toward Robust Multimodal Learning using Multimodal Foundational Models) to address the issue of randomly missing modalities in multimodal sentiment analysis. Recent CLIP-based models have shown impressive performance on various multimodal tasks, but they are not designed to handle scenarios with modality absence. To fill this gap, TRML employs generated virtual modalities to replace missing modalities and aligns their semantic spaces. The framework consists of two modules: a missing modality inference module that generates virtual modalities and replaces missing ones, and a semantic matching learning module that aligns the semantic spaces between generated and missing modalities. Experiment results demonstrate the superiority of TRML on three benchmark datasets for multimodal sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRML is a new way to analyze how people feel about things like movies or politicians based on multiple types of data, such as text and images. Right now, most models only work well when they have all the necessary data, but in real life, this data can be missing. The authors of TRML created a system that can fill in these gaps by generating fake data that matches what’s already there. This helps the model understand how people feel even when some information is missing. Tests show that TRML works better than other models on three types of datasets. |
Keywords
» Artificial intelligence » Inference
