回答,快与慢》:因果关系引导下的视觉理解策略提升

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chao Wang , Zihao Wang , Yang Zhou
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引用次数: 0

摘要

卡尼曼在其经典著作《思考,快与慢》(丹尼尔,2017)中指出,人类思维可分为两大思维模式:一个是展现直觉和情感的系统(即系统1),另一个是更有计划、更依赖逻辑的系统,被定义为系统2。这一观点同时解释了理性和非理性动机。在本文中,我们将根据这一观点重新审视视觉理解任务。在理论层面,我们关注直觉思维、先验知识和环境信息之间的关系,并构建了三者之间的因果关系图。此外,在所构建的因果图的启发下,我们提出了一种具有明确可解释性的直觉优化策略。在验证环节,我们通过在基于视觉测验任务的公共数据集上进行大量实验,得出了与理论分析一致的结论。令人兴奋的是,在不增加新技术的情况下,我们的方案在通用性方面表现出了很强的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Answering, Fast and Slow: Strategy enhancement of visual understanding guided by causality
In his classic book Thinking, Fast and Slow (Daniel, 2017), Kahneman points out that human thinking can be categorized into two main modes of thinking: a system that displays intuition and emotion (i.e., System 1), and a system that is more planned and relies more on logic, defined as System 2. This idea explains both rational and irrational motivations. In this paper, we revisit visual comprehension tasks based on this idea. At the theoretical level, we focus on the relationship between intuitive thinking, prior knowledge, and environmental information, and build a causal graph between the three. Further, inspired by the constructed causal graph, an intuitive optimization strategy with clear interpretability is proposed. In the validation session, we provide conclusions consistent with the theoretical analyses through extensive experiments on public datasets based on a visual quizzing task. Excitingly, our scheme demonstrates strong competitiveness in terms of generalizability without adding new technologies.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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