Summary of Solo: a Single Transformer For Scalable Vision-language Modeling, by Yangyi Chen et al.
SOLO: A Single Transformer for Scalable Vision-Language Modeling
by Yangyi Chen, Xingyao Wang, Hao Peng, Heng Ji
First submitted to arxiv on: 8 Jul 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 This paper presents SOLO, a single transformer architecture for Scalable visiOn-Language mOdeling that addresses four primary scalability limitations of current large vision-language models (LVLMs). Specifically, SOLO tackles constraints on visual capacity, heterogeneous architectures, and the need for specific image preprocessing. The authors introduce an open-source training recipe for developing SOLO, which involves initializing from language models, sequential pre-training, and instruction fine-tuning. On extensive evaluation, SOLO demonstrates comparable performance to LLaVA-v1.5-7B, particularly excelling in visual mathematical reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to connect vision and language together in computers. The old way uses separate parts for seeing pictures and understanding words, but this limits how well it can do certain tasks. The new approach, called SOLO, tries to fix these problems by using a single “transformer” that can handle both pictures and words. To make this work, the authors created a special recipe for training SOLO, which involves teaching it on lots of different images and text. This new way of connecting vision and language is really good at solving certain types of math problems. |
Keywords
* Artificial intelligence * Fine tuning * Transformer
