Summary of Missing Modality Prediction For Unpaired Multimodal Learning Via Joint Embedding Of Unimodal Models, by Donggeun Kim and Taesup Kim
Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models
by Donggeun Kim, Taesup Kim
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed framework integrates parameter-efficient fine-tuning of unimodal pretrained models with a self-supervised joint-embedding learning method to predict the embedding of a missing modality in the representation space during inference. This approach leverages information from available modalities through prompt tuning, enabling the model to effectively predict the missing embedding. The framework is evaluated on several multimodal benchmark datasets, demonstrating its effectiveness and robustness across various scenarios of missing modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in machine learning called “missing modalities”. It means that sometimes we can’t get all the data we need for training or using our models. The researchers came up with a new way to deal with this issue by fine-tuning models and learning how to predict missing information. They tested their approach on different datasets and showed it works well in many situations. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Inference » Machine learning » Parameter efficient » Prompt » Self supervised
