# Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms

arXiv: https://arxiv.org/abs/2312.12872 · HTML: https://mlsanity.com/paper/2312.12872/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods](https://mlsanity.com/paper/1907.09358/) (2019)

## Built on by (most cited first, 19 of 19)

- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)
- [Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data](https://mlsanity.com/paper/2402.09830/) (2024)
- [Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models](https://mlsanity.com/paper/2402.12916/) (2024)
- [Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis](https://mlsanity.com/paper/2402.15994/) (2024)
- [Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data](https://mlsanity.com/paper/2402.15796/) (2024)
- [Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management](https://mlsanity.com/paper/2402.17216/) (2024)
- [Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations](https://mlsanity.com/paper/2402.16035/) (2024)
- [Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization](https://mlsanity.com/paper/2403.07905/) (2024)
- [Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research](https://mlsanity.com/paper/2402.16038/) (2024)
- [Enhanced User Interaction in Operating Systems through Machine Learning Language Models](https://mlsanity.com/paper/2403.00806/) (2024)
- [Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks](https://mlsanity.com/paper/2403.03165/) (2024)
- [Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis](https://mlsanity.com/paper/2403.08217/) (2024)
- [Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation](https://mlsanity.com/paper/2403.09718/) (2024)
- [Utilizing Deep Learning for Enhancing Network Resilience in Finance](https://mlsanity.com/paper/2402.09820/) (2024)
- [Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning](https://mlsanity.com/paper/2403.13619/) (2024)
- [Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation](https://mlsanity.com/paper/2603.15134/) (2026)
- [Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations](https://mlsanity.com/paper/2403.02760/) (2024)
- [Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning](https://mlsanity.com/paper/2403.18100/) (2024)
- [Locally Private Online Quantile Regression: Estimation and Inference](https://mlsanity.com/paper/2607.05312/) (2026)

## Abstract (arXiv)

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.