群组视觉关系检测检测 视觉关系检测
概括
本文介绍了组视觉关系检测 (GVRD),用于识别图像中涉及组的关系. 拟议的同时群关系预测 (SGRP) 方法有效地检测这些群视觉关系 (GVR).
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:
- 现有的视觉关系检测专注于单个对象,忽视群体交互.
- 群体在图像语义中很普遍,需要先进的检测方法.
- 组视觉关系检测 (GVRD) 扩展了传统方法,包括组主体和/或对象.
研究的目的:
- 为了介绍一个新的任务,组视觉关系检测 (GVRD).
- 提出一种新的方法,即同时进行组关系预测 (SGRP),用于解决GVRD.
- 创建和发布一个新的数据集,COCO-GVR,用于GVRD研究.
主要方法:
- 建议使用同步组关系预测 (SGRP) 方法.
- SGRP包括实体构建 (EC),特征提取 (FE) 和组关系预测 (GRP) 模块.
- 该EC模块生成实例和组/短语候选;FE提取多模式特征;GRP同时预测组和预测.
主要成果:
- 创建了新的COCO-GVR数据集,包含9570张图像和31855张GVR.
- 在COCO-GVR数据集上进行了广泛的实验.
- 与基线方法相比,SGRP方法显示出更高的性能.
结论:
- GVRD是视觉关系检测的一个有价值的扩展.
- 该SGRP方法有效地解决了GVRD任务.
- COCO-GVR数据集有助于未来对群体视觉关系检测的研究.
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