文本指导如何影响视觉注意力? 数据库和模型
概括
本研究介绍了以文本为指导的图像突出度 (TIS) 预测,分析了文本如何影响视觉注意力. 一个新的数据集和模型 (TGSal) 通过整合图像和文本功能显著改善了突出性预测.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人与计算机的交互
背景情况:
- 视觉注意力和突出性预测是计算机视觉中的关键.
- 现有的模型往往忽略了附带文本描述对视觉注意力的重大影响.
研究的目的:
- 为了全面调查文本导向图像突出性 (TIS).
- 开发和评估一种新的突出性预测模型,该模型包含文本指导.
主要方法:
- 构建SJTU-TIS数据库,其中包含1200个文字图像对和眼睛跟踪数据.
- 分析文本描述对视觉注意力的影响.
- 关于一个整合图像和文本特征的文本导向突出性 (TGSal) 预测模型的建议.
主要成果:
- 该SJTU-TIS数据库为TIS研究提供了一个基准.
- 拟议的TGSal模型在TIS和纯图像突出性数据集上的最先进模型中表现出卓越的性能.
- TGSal有效地利用视觉和文本信息进行准确的突出预测.
结论:
- 文字描述显著影响视觉注意力和突出性.
- TGSal模型代表了以文本为导向的视觉突出性预测的实质性进展.
- 发布的数据集和模型将促进该领域的进一步研究.
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