[论文解读] Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms

Bo Liu, Liqiang Yu+4 more|arXiv (Cornell University)|Dec 20, 2023

Advanced Technologies in Various Fields被引用 51

一句话总结

该论文分析将深度学习与计算机视觉结合在图像分类和目标检测等任务中的改进，同时强调泛化和可解释性方面的挑战，并提出未来方向。

ABSTRACT

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries.

研究动机与目标

• 评估将深度学习与计算机视觉集成以实现端到端特征学习和语义理解的有效性。
• 评估深度神经网络在分类和检测任务中如何超过传统计算机视觉方法。
• 识别在泛化和可解释性方面的局限性，并提出未来改进方向。

提出的方法

• 对典型图像分类案例进行回顾与分析，以证明深度神经网络模型的优越性。

实验结果

研究问题

• RQ1 在典型图像分类场景中，将深度学习与计算机视觉结合的效果有多大？
• RQ2 基于深度学习的计算机视觉系统在泛化和可解释性方面的主要局限性是什么？
• RQ3 在整合人工智能与计算机视觉方面，未来改进的方向有哪些？

主要发现

• 深度学习实现了端到端的特征学习和语义理解，在若干任务中超过了传统计算机视觉方法。
• 根据分析，将深度学习融入的人工智能与计算机视觉在图像分类和目标检测方面表现出更优越的性能。
• 深度神经网络在泛化和可解释性方面存在局限性，指出未来需要改进的领域。
• 与大规模视觉数据的深度学习持续整合有望推动持续突破和更广泛的应用。