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Compositional Visual Learning and Reasoning in Humans and Machines
Compositional Visual Learning and Reasoning in Humans and Machines
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152706
- ISBN
- 9798342709873
- DDC
- 153
- 저자명
- Zhou, Yanli.
- 서명/저자
- Compositional Visual Learning and Reasoning in Humans and Machines
- 발행사항
- [Sl] : New York University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 103 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Lake, Brenden M.
- 학위논문주기
- Thesis (Ph.D.)--New York University, 2024.
- 초록/해제
- 요약Humans leverage compositionality to efficiently learn new visual concepts, understanding how familiar parts can be combined to form novel objects. Moreover, our conceptual knowledge of these novel objects is often enhanced by understanding how parts relate to their functions and how composing the parts relates to composing the functions. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do.In this thesis, we explore two aspects of human visual learning and reasoning. We first study the unique human capability to learn visual concepts across a range of different types of visual composition, examining how people classify and generate a novel class of abstract visual concepts with rich relational structures. We also examine how functions are learned and represented by humans from limited visual observations and the ability to perform zero-shot function compositions in various interaction conditions. In all studies, we compare humans with computational models directly on the same set of learning tasks, shedding light on whether computational models, including Bayesian program induction models and meta-learning neural networks, can perform compositional visual concept and function learning in the complete and complex way humans do.
- 일반주제명
- Cognitive psychology
- 일반주제명
- Computer science
- 일반주제명
- Psychology
- 키워드
- Compositionality
- 키워드
- Concept learning
- 키워드
- Meta-learning
- 키워드
- Reasoning
- 기타저자
- New York University Center for Data Science
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798342709873
■035 ▼a(MiAaPQ)AAI31488377
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a153
■1001 ▼aZhou, Yanli.
■24510▼aCompositional Visual Learning and Reasoning in Humans and Machines
■260 ▼a[Sl]▼bNew York University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a103 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Lake, Brenden M.
■5021 ▼aThesis (Ph.D.)--New York University, 2024.
■520 ▼aHumans leverage compositionality to efficiently learn new visual concepts, understanding how familiar parts can be combined to form novel objects. Moreover, our conceptual knowledge of these novel objects is often enhanced by understanding how parts relate to their functions and how composing the parts relates to composing the functions. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do.In this thesis, we explore two aspects of human visual learning and reasoning. We first study the unique human capability to learn visual concepts across a range of different types of visual composition, examining how people classify and generate a novel class of abstract visual concepts with rich relational structures. We also examine how functions are learned and represented by humans from limited visual observations and the ability to perform zero-shot function compositions in various interaction conditions. In all studies, we compare humans with computational models directly on the same set of learning tasks, shedding light on whether computational models, including Bayesian program induction models and meta-learning neural networks, can perform compositional visual concept and function learning in the complete and complex way humans do.
■590 ▼aSchool code: 0146.
■650 4▼aCognitive psychology
■650 4▼aComputer science
■650 4▼aPsychology
■653 ▼aBayesian modeling
■653 ▼aCognitive modeling
■653 ▼aCompositionality
■653 ▼aConcept learning
■653 ▼aMeta-learning
■653 ▼aReasoning
■690 ▼a0633
■690 ▼a0984
■690 ▼a0621
■71020▼aNew York University▼bCenter for Data Science.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0146
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163427▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
