用户任务预测研究进展与算法分析

Research progress of user task prediction and algorithm analysis

用户在执行不同任务时,会表现出不同的感知行为。知道用户正在执行的任务可以帮助进行用户行为的分析,也可以作为智能交互系统的输入,使得系统自动根据用户不同的任务提供不同的功能,改善用户的体验。用户任务预测指的是根据用户的眼睛运动特征、场景内容特征等相关信息来预测用户正在执行的任务。用户任务预测是视觉研究领域中的一个热门研究课题,研究者们针对不同的场景提出了很多有效的任务预测算法。然而,以往工作中提出的算法大多是针对一种特定类型的场景,且不同算法之间缺乏统一的测试和分析。本文首先回顾了图片场景、视频场景、以及现实场景中用户任务预测问题的相关进展,接着对目前主要的任务预测算法进行了详细的介绍。并在一个现实场景任务数据集上对相关算法进行了测试和分析,为未来的相关研究提供了有意义的见解。

Users’cognitive behaviors are dramatically influenced by the specific tasks assigned to them.Information on users’tasks can be applied to many areas,such as human behavior analysis and intelligent human-computer interfaces.It can be used as the input of intelligent systems and enable the systems to automatically adjust their functions according to different tasks.User task prediction refers to the prediction of users’tasks at hand based on the characteristics of his or her eye movements,the characteristics of scene content,and other related information.User task prediction is a popular research topic in vision research,and researchers have proposed many successful task prediction algorithms.However,the algorithms proposed in prior works mainly focus on a particular scene,and comparison and analysis are absent for these algorithms.This paper presented a review of prior works on task prediction in scenes of images,videos,and real world,and detailed existing task prediction algorithms.Based on a real-world task dataset,this paper evaluated the performances of existing algorithms and conducted the corresponding analysis and discussion.As such,this work can provide meaningful insights for future works on this important topic.