Summary of Emma: Efficient Visual Alignment in Multi-modal Llms, by Sara Ghazanfari et al.
EMMA: Efficient Visual Alignment in Multi-Modal LLMs
by Sara Ghazanfari, Alexandre Araujo, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 EMMA, a lightweight cross-modality module for efficiently fusing visual and textual encodings in Multi-modal Large Language Models (MLLMs). The goal is to improve task-specific adaptability without increasing model complexity or training data needs. The authors introduce an early fusion mechanism that integrates vision and language representations with minimal added parameters, making it more efficient than existing methods. They also provide an interpretability analysis and demonstrate notable improvements on specialized and general benchmarks for MLLMs, boosting performance by up to 9.3% while improving robustness against hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to combine images and text in large language models. These models are getting really good at understanding and generating text, but they can also use visual information like pictures or videos to make them even better. The problem is that combining these two types of information can be tricky, and the current methods aren’t very efficient. The authors have come up with a new approach called EMMA that makes it easier and more effective to combine images and text. They tested it on different tasks and showed that it works really well, improving performance by as much as 9% while also making the models more robust. |
Keywords
» Artificial intelligence » Boosting » Multi modal
