Summary of Provision: Programmatically Scaling Vision-centric Instruction Data For Multimodal Language Models, by Jieyu Zhang et al.
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
by Jieyu Zhang, Le Xue, Linxin Song, Jun Wang, Weikai Huang, Manli Shu, An Yan, Zixian Ma, Juan Carlos Niebles, Silvio Savarese, Caiming Xiong, Zeyuan Chen, Ranjay Krishna, Ran Xu
First submitted to arxiv on: 9 Dec 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 paper presents a novel approach to generating vision-centric instruction data for training multimodal language models. It employs scene graphs as symbolic representations of images and human-written programs to systematically synthesize instruction data. The approach ensures the interpretability and controllability of the data generation process, scales efficiently, and maintains factual accuracy. The system, called ProVision, produces diverse question-answer pairs concerning objects, attributes, relations, depth, etc., for any given image. It generates over 10 million instruction data points, ProVision-10M, which are used in both pretraining and instruction tuning stages of multimodal language models. The paper demonstrates the effectiveness of the approach by showing improvements on various benchmarks, including CVBench, QBench2, RealWorldQA, MMMU, and Mantis-Eval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make computers understand complex images. It uses special codes called scene graphs to help machines learn from pictures. This approach makes it easier for computers to generate instructions that are accurate and relevant to the image. The system produces millions of questions and answers about objects, attributes, and relationships in images. The generated data is used to improve the performance of multimodal language models on various tasks. |
Keywords
» Artificial intelligence » Instruction tuning » Pretraining
