关系LMM:大型多式模式作为开放和多功能视觉关系通用主义者
概括
本研究介绍了RelationLMM,这是一种用于视觉关系任务的统一框架. 它通过改革数据来增强大型多式模式 (LMM),使单个模型能够在各种视觉关系挑战中进行概括.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 视觉关系是感知和推理的基础,支持诸如场景图表生成和人与物体交互等任务.
- 当前的方法往往缺乏任务概括,采用严格的表述,并未充分利用基于语言的推理.
- 大型多式模式 (LMM) 显示出统一的希望,但由于任务冲突和实例细节不足,在视觉关系任务中扎.
研究的目的:
- 开发一个统一的框架,RelationLMM,能够处理使用大型多式模式 (LMM) 的各种视觉关系任务.
- 通过为多个视觉关系任务创建一个单一的灵活模型来克服现有方法的局限性.
- 为了利用LMMs的语言输入,语言输出能力来改进视觉推理.
主要方法:
- 改革LMM的数据,重点是任务统一和实例级信息,而不是架构变化.
- 将复杂的任务分解成更简单,更常见的子任务.
- 在没有额外的模块的情况下,口头估计实例信心并增加实例多样性.
主要成果:
- 关系LMM在各种视觉关系任务中表现出强大的性能,可概括性和灵活性.
- 提出的以数据为中心的方法有效地解决了LMM中的任务冲突和不足的实例信息.
- 一个单一的模型具有统一的权重集,可以实现多个专业任务的熟练.
结论:
- 关系LMM建立了一个新的,数据驱动的方法来增强视觉关系理解的LMM.
- 该框架提供了一种灵活和可通用的解决方案,将多样化的任务统一在一个单一模型下.
- 这项工作突出了LMM数据改造的潜力,以提高视觉推理能力.
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