Guiding Attention in Real-World Scenes

在现实场景中引导注意力

基本信息

批准号：
10477474
负责人：
John M Henderson
金额：
$ 36.89万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10477474
关键词：
Address Artificial Intelligence Attention Basic Science Behavior Behavioral Brain Cognition Cognitive Science Complex Computer Vision Systems Data Development Environment Esthetics Evaluation Funding Goals Health Human Hybrids Image Individual Investigation Judgment Knowledge Lead Maps Marshal Methods Modeling Nature Neural Network Simulation Neurologic Outcome Perception Play Population Process Property Quality of life Research Resolution Role Semantics Services Spatial Distribution Structure Testing Ursidae Family Vision Visual Visual Perception Visual attention Work base behavior test concept mapping convolutional neural network crowdsourcing deep learning model digital imaging experimental study grasp image processing imaging properties innovation insight model development rehabilitation strategy visual information visual search

项目摘要

Project Summary Real-world scenes contain far more information than we can perceive at any given moment. Scene perception therefore requires attentional selection of relevant scene regions for prioritized processing. How are those aspects of the world that should receive priority selected? Although much past research has focused on how attention is guided by the visual properties of a scene, new evidence from meaning maps, developed in the previous funding period, established that the distribution of meaning across a scene plays a central and often dominant role in guiding attention. This surprising finding raises many important new questions about the nature of scene meaning and its specific role in attentional guidance. The overarching goal of this project is to understand in detail how the semantic features of a scene’s objects and functional spaces influence the guidance of visual attention in complex real-world scenes. The specific aims are: (1) To determine the role of object semantics in attentional guidance in scenes; (2) To determine the role of functional spaces in attentional guidance in scenes; (3) To determine how viewing task interacts with scene semantics in guiding attention. The project is innovative in expanding the traditional study of attention to explicitly consider the role of meaning. To this end, new semantic maps capitalizing on the meaning map concept will be used capture local region meaning continuously over a scene, allowing for direct investigation of the relationships of different types of meaning with attention. The project is innovative in (1) expanding the traditional study of visual attention to explicitly consider the role of semantics; (2) focusing on the semantics of both scene content (objects) and scene structure (space); (3) considering the role of meaning in attentional guidance in the context of viewing task; (4) integrating the use of a wide variety of cognitive science methods marshalled in the service of understanding the influence of meaning on visual attention in real-world scenes, including eyetracking, large-scale crowd-sourcing, computational image processing, computational semantic modeling, and deep convolutional neural networks. The project is significant in challenging current models of visual attention to account for the role of scene meaning. Because the proposed studies test competing models, the results will lead to the development of integrative theoretical frameworks that advance the field regardless of the outcome. While focused on basic science, the studies have potentially important translational implications by providing a more complete characterization of the processes associated with visual attention. The proposed studies may ultimately lead to the development of rehabilitation strategies for visual attention as it operates in the real world, better capitalizing on the use of a viewer’s knowledge to offset disrupted functions in those with attention and vision deficits.

项目概要现实世界的场景包含的信息远多于我们在任何给定时刻所能感知的信息。因此区域需要注意选择相关场景进行优先处理。尽管过去的许多研究都集中在如何选择世界的哪些方面？注意力是由场景的视觉属性引导的，来自意义地图的新证据，在之前的资助期间，确定了整个场景的意义分布起着核心作用，并且经常发挥作用这一令人惊讶的发现提出了许多关于注意力的重要新问题。场景意义的本质及其在注意力引导中的具体作用该项目的总体目标是详细了解场景对象和功能空间的语义特征如何影响复杂现实场景中视觉注意力的指导具体目标是：（1）确定视觉注意力的作用。场景中注意引导中的对象语义；（2）确定功能空间在注意中的作用；场景中的引导；（3）确定观看任务如何与场景语义相互作用来引导注意力。该项目的创新之处在于扩展了传统的注意力研究，明确考虑了为此，将使用利用意义图概念的新语义图来捕获局部意义。区域在场景中具有连续意义，允许直接调查不同区域之间的关系该项目的创新之处在于（1）扩展了传统的视觉研究。注意明确考虑语义的作用；（2）关注场景内容的语义；（物体）和场景结构（空间）；（3）考虑上下文中注意力引导的意义的作用； (4) 整合服务中整理的各种认知科学方法的使用了解意义对现实世界场景中视觉注意力的影响，包括眼球追踪，大规模众包、计算图像处理、计算语义建模和深度学习该项目对于挑战当前的视觉注意力模型具有重要意义。由于所提出的研究测试了竞争模型，因此结果将说明场景意义的作用。导致综合理论框架的发展，无论结果如何，该框架都会推动该领域的发展。这些研究虽然侧重于基础科学，但通过提供拟议的研究可能会更完整地描述与视觉注意相关的过程。最终导致视觉注意力康复策略的发展，因为它在现实中运作世界，更好地利用观众的知识来抵消那些注意力集中的人的功能中断和视力缺陷。