Summary of Multimodal Pathway: Improve Transformers with Irrelevant Data From Other Modalities, by Yiyuan Zhang et al.
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
by Yiyuan Zhang, Xiaohan Ding, Kaixiong Gong, Yixiao Ge, Ying Shan, Xiangyu Yue
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, Multimodal Pathway, aims to improve transformers designed for a specific modality by incorporating irrelevant data from other modalities. Unlike paired or interleaved multimodal data used in other works, the target modality’s data samples are irrelevant to the other modalities. The approach utilizes the universal sequence-to-sequence modeling abilities of transformers obtained from two modalities. A concrete implementation uses a modality-specific tokenizer and task-specific head, while utilizing transformer blocks of an auxiliary model via Cross-Modal Re-parameterization. This method exploits auxiliary weights without inference costs. Experiments on image, point cloud, video, and audio recognition tasks show significant and consistent performance improvements with irrelevant data from other modalities. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful tools for processing different types of data. A team of researchers wants to make them even better by using information that might seem useless at first. They’re talking about data from a different type, like images if they’re working with audio or point clouds. This is different from other methods that use paired or mixed data. Instead, the researchers are proposing a new way called Multimodal Pathway. It connects two models together to process data in a unique way. They tested this method on various tasks and saw significant improvements. |
Keywords
* Artificial intelligence * Inference * Tokenizer * Transformer