Summary of A Wander Through the Multimodal Landscape: Efficient Transfer Learning Via Low-rank Sequence Multimodal Adapter, by Zirun Guo et al.
A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal Adapter
by Zirun Guo, Xize Cheng, Yangyang Wu, Tao Jin
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 an efficient transfer learning method called loW-rank sequence multimodal adapter (Wander) to address challenges in fine-tuning multimodal models. Existing methods are limited by their design for vision-language tasks, lack exploitation of modality interactions, and exhibit poor efficiency. Wander addresses these issues through a combination of techniques: outer product-based fusion, CP decomposition for parameter reduction, and token-level low-rank decomposition for extracting fine-grained features. The authors conduct extensive experiments on datasets with varying numbers of modalities, demonstrating that Wander outperforms state-of-the-art methods consistently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make computers better at understanding different types of information from various sources. Currently, computers are great at learning about pictures and words together, but they struggle when there’s more than two types of information involved. The researchers developed a new way called Wander that lets computers learn efficiently and effectively from multiple types of information. They tested it on many different datasets and found that it worked better than previous methods. |
Keywords
» Artificial intelligence » Fine tuning » Token » Transfer learning
