Summary of Longllava: Scaling Multi-modal Llms to 1000 Images Efficiently Via a Hybrid Architecture, by Xidong Wang et al.
LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture
by Xidong Wang, Dingjie Song, Shunian Chen, Chen Zhang, Benyou Wang
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel approach to improve the capabilities of Multi-modal Large Language Models (MLLMs) in understanding videos and high-resolution images. The goal is to address challenges like decreased performance with more images and high computational costs. To achieve this, the authors adapt the model architecture to a hybrid of Mamba and Transformer blocks, construct data with temporal and spatial dependencies among multiple images, and employ a progressive training strategy. The resulting model, LongLLaVA, achieves competitive results on various benchmarks while maintaining high throughput and low memory consumption. This has promising application prospects for tasks that require processing large amounts of visual data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines better at understanding videos and pictures. Right now, they’re not very good at it. The researchers want to change this by improving the way these machines learn from lots of images. They did some clever things like combining different learning techniques and adding special features that help with tricky tasks. The result is a new machine that can understand many more images than before and does it quickly and efficiently. This is important because it could be used for all sorts of cool applications, like helping robots see and understand the world around them. |
Keywords
» Artificial intelligence » Multi modal » Transformer
