用贝叶斯决策理论统一计算机视觉与认知科学的视角。
Computer Vision and Its Relationship to Cognitive Science: A perspective from Bayes Decision Theory
- 以贝叶斯决策理论为框架，整合贝叶斯推理与深度神经网络方法。
- 揭示了两种方法在理论与实践上的互补性与局限性。
- 适合对视觉认知机制和模型融合感兴趣的学者与工程师。
本文从贝叶斯决策理论（Berger, 1985）的视角出发，介绍计算机视觉及其与认知科学的关系。计算机视觉领域广泛而复杂，本文聚焦于一个理论视角，涵盖多个核心概念。该理论框架丰富，包含两种不同方法：(i) 贝叶斯观点，其概念上吸引人，与认知科学中的认知机制高度共鸣（Griffiths et al., 2024）；(ii) 深度神经网络方法，其在现实世界中的成功推动计算机视觉发展为价值超万亿的产业，并受到视觉腹侧通路层级结构的启发。贝叶斯决策理论能够关联并捕捉这两种方法的优势与不足，通过分析其局限性，指明未来可构建更综合的理论框架。
原文摘要 · Abstract (English)
This document presents an introduction to computer vision, and its relationship to Cognitive Science, from the perspective of Bayes Decision Theory (Berger 1985). Computer vision is a vast and complex field, so this overview has a narrow scope and provides a theoretical lens which captures many key concepts. BDT is rich enough to include two different approaches: (i) the Bayesian viewpoint, which gives a conceptually attractive framework for vision with concepts that resonate with Cognitive Science (Griffiths et al., 2024), and (ii) the Deep Neural Network approach whose successes in the real world have made Computer Vision into a trillion-dollar industry and which is motivated by the hierarchical structure of the visual ventral stream. The BDT framework relates and captures the strengths and weakness of these two approaches and, by discussing the limitations of BDT, points the way to how they can be combined in a richer framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。