Summary of Robust Latent Representation Tuning For Image-text Classification, by Hao Sun and Yu Song
Robust Latent Representation Tuning for Image-text Classification
by Hao Sun, Yu Song
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 paper proposes a novel approach to enhance large language models with multimodal processing abilities, addressing challenges posed by scenarios where one modality is absent. A robust latent representation tuning method is introduced, comprising a modality latent translation module to maximize correlation between modalities and a fusion module for information interaction. The framework refines common semantics during training, achieving robust performance even without one modality. Importantly, the model preserves the capabilities of frozen foundation models acquired through large-scale pretraining. Experiments on public datasets demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make big language models better at understanding different types of information, like images and text. They have a new way of doing this that works even when one type of information is missing. This helps the model learn more from what it has, making it more useful in real-life situations. The idea is to keep the strengths of the original model while improving its ability to handle different types of data. |
Keywords
» Artificial intelligence » Pretraining » Semantics » Translation
