Authors: Xiangxiang Chu, Limeng Qiao, Xinyu Zhang, Shuang Xu, Fei Wei, Yang Yang, Xiaofei Sun, Yiming Hu, Xinyang Lin, Bo Zhang, Chunhua Shen

Published on: February 06, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.03766

Summary

• What is new: MobileVLM V2 introduces significant improvements in vision language models (VLMs) for mobile devices, outperforming much larger VLMs with a novel design, training scheme, and dataset curation.
• Why this is important: Existing mobile VLMs face performance limitations due to their design, training schemes, and the quality of datasets used.
• What the research proposes: MobileVLM V2 utilizes a new architectural design, an improved training scheme specifically tailored for mobile VLMs, and a curation of rich, high-quality datasets.
• Results: MobileVLM V2 1.7B achieves on-par or better performance compared to VLMs at the 3B scale, and the 3B model surpasses several VLMs above 7B scale in standard benchmarks.

Technical Details

Technological frameworks used: nan

Models used: MobileVLM V2, including versions 1.7B and 3B.

Data used: Curated high-quality datasets specific for VLM training.

Potential Impact

Mobile technology, app development, and companies invested in AI-driven image and language processing could be significantly impacted or benefit from these insights.

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