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SGTR+:用变压器进行端到端场景图谱生成.
IEEE transactions on pattern analysis and machine intelligence
|November 13, 2023
概括
本研究介绍了一种基于变压器的新型框架,用于场景图形生成 (SGG),将其视为一个双部分图形构建问题. 新方法实现了最先进的性能,提高了效率.
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:
- 场景图形生成 (SGG) 是一个复杂的视觉理解任务,由于其组成性质.
- 现有的SGG方法经常面临高时间复杂性或低于最佳的设计选择.
- 目前的方法包括自下而上,两阶段或基于点的,一阶段的方法.
研究的目的:
- 提出一种新且高效的场景图生成 (SGG) 方法.
- 解决现有 SGG 方法的局限性,例如高时间复杂性.
- 为了制定SGG作为一个双部分图形构建问题.
主要方法:
- 为SGG开发了一个基于变压器的端到端框架.
- 该框架生成实体和实体意识的预言建议集.
- 一个图形组装模块推断连接为双部分场景图形构建.
主要成果:
- 提出的方法实现了最佳的性能和时间复杂性.
- 在三个具有挑战性的基准标准上获得了最先进的或可比的结果.
- 与现有方法相比,这种方法显示出更高的推理效率.
结论:
- 双部分图形组装范式为SGG提供了一种有效的方法.
- 增强的实体意识建模和稳定的图形组装有助于实现最佳性能.
- 新的框架为创建场景图提供了更有效和更有效的解决方案.
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