Summary of Emma: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment, by Yifei Xing et al.
EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment
by Yifei Xing, Xiangyuan Lan, Ruiping Wang, Dongmei Jiang, Wenjun Huang, Qingfang Zheng, Yaowei Wang
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Empowering Multi-modal Mamba with Structural and Hierarchical Alignment (EMMA) architecture addresses the limitations of current Mamba-based multi-modal large language models (MLLMs) by enabling them to extract fine-grained visual features. This is achieved through a pixel-wise alignment module that autoregressively optimizes spatial image-level features along with textual tokens, allowing for structural alignment at the image level. Additionally, a multi-scale feature fusion (MFF) module combines multi-scale visual features from intermediate layers, enabling hierarchical alignment at the feature level. Experimental results demonstrate lower latency compared to other Mamba-based MLLMs and nearly four times faster inference speed than transformer-based MLLMs of similar scale. The EMMA model exhibits better cross-modal alignment, reduced hallucination, and enhanced sensitivity to visual details, resulting in superior performance across various multi-modal benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of building computer models is being explored. These models can do lots of things, like understand pictures and words. But they’re not perfect yet. They have trouble understanding certain types of images, which makes them less accurate. To fix this, the authors came up with a new idea called EMMA (Empowering Multi-modal Mamba). It’s like a special tool that helps these models learn more about images. This is done by aligning words and pictures in a way that makes sense. The authors tested their idea on different types of tasks and found that it works really well. Their model is also faster than other similar models, which makes it useful for real-world applications. |
Keywords
» Artificial intelligence » Alignment » Hallucination » Inference » Multi modal » Transformer
