RepSGG:用于场景图生成的实体和关系的新型表示
概括
RepSGG通过使用动态的,以注意力为基础的实体表示和以性能为导向的策略来改善场景图生成,以平衡主导和罕见的关系类,实现最先进的结果.
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) 传统上使用固定大小的实体表示,导致性能和计算权衡.
- 有效和动态的特征提取对于推进SGG能力至关重要.
研究的目的:
- 引入一种新的架构,RepSGG,以实现更有效和高效的场景图形生成.
- 解决固定大小表示和SGG中长尾分布的挑战.
主要方法:
- 提议的RepSGG架构:主体作为查询,对象作为关键,关系作为最大关注重量.
- 在培训期间开发了一种以绩效为导向的逻辑调整 (PGLA) 策略,以平衡主导关系类和罕见关系类.
主要成果:
- RepSGG展示了对实体和关系的优越细粒度和灵活的代表力.
- 在Visual Genome和Open Images V6数据集上实现了最先进的或可比的性能.
- 展示了快速的推断速度,突出了该方法的效率.
结论:
- RepSGG提供了一种有效和高效的场景图形生成方法.
- 动态表示和PGLA策略显著提高了SGG的性能和概括性,特别是在长尾分布中.
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