超越LLaVA-HD:潜入高分辨率多模式大语言模型
IEEE transactions on pattern analysis and machine intelligence
|January 6, 2026
概括
多模大型语言模型 (MLLMs) 现在可以通过使用更少,更具信息性的本地图像令牌来实现更好的视觉推理. 这种新方法,SliME,降低了计算成本,提高了复杂任务的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 高分辨率视觉对于多模态大语言模型 (MLLMs) 在视觉感知和推理方面至关重要.
- 目前在MLLM中处理高分辨率图像的方法在计算上昂贵,并且可以稀释全球上下文.
研究的目的:
- 解决MLLM中现有的高分辨率图像处理的计算和上下文限制.
- 提出一个新的框架和优化战略,以提高MLLMs高分辨率视觉理解的效率和有效性.
主要方法:
- 使用混合适配器来提取全球上下文信息.
- 可学习的查询嵌入和基于相似性的选择器减少并选择信息化的本地图像令牌.
- 一个交替的培训策略平衡了全球和本地特征学习,并补充了当地压缩培训的新数据集.
主要成果:
- 经验结果显示了"少就多"效应,更少,更具信息性的本地代币可以提高性能.
- 拟议的SliME框架在有限的培训数据 (200万) 的基准指标中取得了领先的表现.
- 交替培训策略证明比同时进行端到端培训更有效.
结论:
- SliME框架为MLLM中的高分辨率图像处理提供了高效和有效的解决方案.
- 优化的令牌选择和培训策略是提高MLLM在视觉任务中的性能的关键.
- 拟议的方法证明了MLLM能力的显著进步,并降低了计算开销.
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