标题： MagicVL-2B：通过课程学习在移动设备上使用轻量级视觉编码器增强视觉语言模型 显示英文标题

标题： MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning

摘要： 视觉-语言模型（VLMs）近年来取得了显著的突破，使日常生活中各种各样的应用成为可能。 然而，VLMs巨大的计算和存储需求对其在移动设备上的高效部署构成了重大挑战，而移动设备是当今最普遍和易于访问的计算平台。 在本工作中，我们介绍了MagicVL-2B，这是一种专为旗舰智能手机优化的新颖VLM。 MagicVL-2B采用了一个轻量级的视觉编码器，参数少于100M，并具有一种重新设计的动态分辨率方案，可以自适应地生成图像标记，而无需对图像尺寸进行过度修改。 为了进一步提升这种紧凑编码器在VLMs中的性能，我们提出了一种多模态课程学习策略，在训练过程中逐步增加任务难度和数据信息密度。 这种方法显著提高了模型在各种子任务上的性能。 在标准VLM基准上的广泛评估表明，MagicVL-2B在保持与当前最先进模型相当的准确性的同时，将设备上的功耗降低了41.1%。 这些结果确立了MagicVL-2B作为现实世界移动视觉-语言应用的实用且稳健的解决方案，使先进的多模态智能可以直接在智能手机上运行。 显示英文摘要

摘要： Vision-Language Models (VLMs) have achieved remarkable breakthroughs in recent years, enabling a diverse array of applications in everyday life. However, the substantial computational and storage demands of VLMs pose significant challenges for their efficient deployment on mobile devices, which represent the most ubiquitous and accessible computing platforms today. In this work, we introduce MagicVL-2B, a novel VLM meticulously optimized for flagship smartphones. MagicVL-2B leverages a lightweight visual encoder with fewer than 100M parameters and features a redesigned dynamic resolution scheme that adaptively generates image tokens without excessive modification of image dimensions. To further enhance the performance of this compact encoder within VLMs, we propose a multimodal curriculum learning strategy that incrementally increases task difficulty and data information density throughout training. This approach substantially improves the model's performance across a variety of sub-tasks. Extensive evaluations on standard VLM benchmarks demonstrate that MagicVL-2B matches the accuracy of current state-of-the-art models while reducing on-device power consumption by 41.1%. These results establish MagicVL-2B as a practical and robust solution for real-world mobile vision-language applications, enabling advanced multimodal intelligence to run directly on smartphones.