[Paper Review] 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 Fields51 citations

TL;DR

The paper analyzes how integrating deep learning with computer vision improves tasks like image classification and object detection, while highlighting challenges in generalization and interpretability and suggesting future directions.

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.

Motivation & Objective

• Assess the effectiveness of integrating deep learning with computer vision for end-to-end feature learning and semantic understanding.
• Evaluate how deep neural networks outperform traditional computer vision methods in classification and detection tasks.
• Identify limitations in generalization and interpretability and propose future improvement directions.

Proposed method

• Review and analysis of typical image classification cases to demonstrate the superiority of deep neural network models.

Experimental results

Research questions

• RQ1 How effective is the integration of deep learning with computer vision across typical image classification scenarios?
• RQ2 What are the main limitations in generalization and interpretability of deep learning-based computer vision systems?
• RQ3 What directions are proposed for future improvements in integrated AI and computer vision?

Key findings

• Deep learning enables end-to-end feature learning and semantic understanding that surpasses traditional computer vision methods in several tasks.
• The integration of AI and computer vision with deep learning shows superior performance in image classification and object detection according to the analysis.
• Limitations exist in generalization and interpretability of deep neural networks, indicating areas for future improvement.
• Sustained integration of deep learning with large-scale visual data is expected to drive continued breakthroughs and broader applications.