Summary of Multimodal Transformer with a Low-computational-cost Guarantee, by Sungjin Park and Edward Choi
Multimodal Transformer With a Low-Computational-Cost Guarantee
by Sungjin Park, Edward Choi
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 This paper introduces Low-Cost Multimodal Transformer (LoCoMT), a novel attention mechanism designed to reduce computational cost in multimodal Transformers while minimizing performance loss. The current state-of-the-art multimodal Transformers suffer from quadratic complexity, making them inefficient as the number of modalities increases. LoCoMT addresses this issue by assigning different multimodal attention patterns to each attention head, allowing for flexible control over multimodal signals and reduced computational cost. Experimental results on Audioset and MedVidCL demonstrate that LoCoMT not only reduces GFLOPs but also matches or outperforms established models in multimodal understanding tasks such as visual question answering and action recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoCoMT is a new way to make Transformer-based models work better with lots of different kinds of data. Right now, these models are really good at understanding pictures, words, and sounds, but they get slower and less efficient when there’s too much information. The LoCoMT team figured out how to make the model more efficient by letting it focus on different parts of the data at once. This helps reduce the amount of math the computer needs to do, making it faster and better for big projects. |
Keywords
* Artificial intelligence * Attention * Question answering * Transformer
