PiGLET:使用转换器对语言表达式进行像素级接地
概括
这项研究引入了全视叙事接地,这是视觉接地的一种新方法. 拟议的PiGLET模型在这个任务上取得了强的表现,并改善了全光学细分.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 自然语言视觉接地问题旨在将文本描述与图像中的特定区域联系起来.
- 现有的方法往往缺乏复杂场景理解所需的空间细粒度和普遍性.
研究的目的:
- 引入全视叙事接地,这是视觉接地的一种空间细粒度和一般性表述.
- 为此任务提出PiGLET,一种新的多模式变压器架构.
- 建立一个实验框架,提供新的基础真相和评估指标.
主要方法:
- 开发了一种算法,可以自动将注释转移到全视分段.
- 提出了PiGLET,一个利用泛光类别和细粒度细分的多模式变压器.
- 在MS COCO数据集上评估了PiGLET,用于全视叙事接地.
主要成果:
- 皮格莱特在全视叙事接地任务中获得了63.2个绝对平均回忆点.
- 在MS COCO基准指标上,PiGLET提高了0.4个Panoptic质量点的全视分段.
- 证明了对其他视觉接地任务的概括性,例如引用表达式分割.
结论:
- 全视叙事接地提供了一个更全面的视觉接地方法.
- PiGLET是这项任务的有效架构,在现有基准上显示出具有竞争力的表现.
- 拟议的框架有助于未来研究细粒度,一般视觉接地.
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