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Computational Models for Cognitive Vision最新文献

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Index 指数
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.index
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引用次数: 0
Bayesian Reasoning for Perception and Cognition 感知和认知的贝叶斯推理
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch3
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引用次数: 0
Conclusion 结论
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch10
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引用次数: 0
Early Vision 早期的愿景
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch2
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引用次数: 1
Visual Attention 视觉注意力
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch5
G. Logan
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引用次数: 0
Applications of Visual Cognition 视觉认知的应用
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch9
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引用次数: 0
Late Vision 后期视觉
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch4
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引用次数: 0
Deep Learning for Visual Cognition 视觉认知的深度学习
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch8
Hiranmay Ghosh
The reliance on machine learning in cognitive systems is further justified as it is not always possible to equip an intelligent agent with prior knowledge. In the modern times, the field of machine learning has been dominated by deep learning based on artificial neural networks. This chapter introduces deep learning for visual cognition. It starts with a brief introduction to deep neural networks (DNN) and some basic reusable configurations to realize various learning algorithms. The chapter discusses various modes of learning realized through DNN in some illustrative computer vision tasks. It presents a broad overview of the architecture of the networks and the principles involved. The chapter focuses on DNN‐based implementations of visual attention models, which is crucial for alleviating information overload in cognitive systems. It presents some research on synthesizing Bayesian reasoning with DNNs, aimed at improving the robustness of inferences. Finally, the chapter concludes with some salient observations on the topic.
在认知系统中对机器学习的依赖是进一步合理的,因为并不总是有可能为智能代理配备先验知识。在现代,机器学习领域以基于人工神经网络的深度学习为主导。本章介绍了视觉认知领域的深度学习。首先简要介绍深度神经网络(DNN)和一些基本的可重用配置来实现各种学习算法。本章讨论了在一些说明性计算机视觉任务中通过深度神经网络实现的各种学习模式。它对网络的体系结构和所涉及的原则进行了广泛的概述。本章的重点是基于DNN的视觉注意模型的实现,这对于减轻认知系统中的信息过载至关重要。介绍了一些将贝叶斯推理与深度神经网络相结合的研究,旨在提高推理的鲁棒性。最后,本章总结了一些关于这个主题的重要观察。
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引用次数: 3
Cognitive Architectures 认知体系结构
Pub Date : 2020-07-06 DOI: 10.1002/9781119527886.ch6
Stefan Profanter
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引用次数: 0
期刊
Computational Models for Cognitive Vision
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