{"title":"回答,快与慢》:因果关系引导下的视觉理解策略提升","authors":"Chao Wang , Zihao Wang , Yang Zhou","doi":"10.1016/j.neucom.2024.128735","DOIUrl":null,"url":null,"abstract":"<div><div>In his classic book <em>Thinking, Fast and Slow</em> (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.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"613 ","pages":"Article 128735"},"PeriodicalIF":5.5000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Answering, Fast and Slow: Strategy enhancement of visual understanding guided by causality\",\"authors\":\"Chao Wang , Zihao Wang , Yang Zhou\",\"doi\":\"10.1016/j.neucom.2024.128735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In his classic book <em>Thinking, Fast and Slow</em> (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.</div></div>\",\"PeriodicalId\":19268,\"journal\":{\"name\":\"Neurocomputing\",\"volume\":\"613 \",\"pages\":\"Article 128735\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2024-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurocomputing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925231224015066\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224015066","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.