Keywords
Computer Vision
Convolutional Neural Networks
How to Cite
Abstract
Deep learning has revolutionized computer vision by significantly improving the performance of various tasks such as object detection, recognition, and segmentation. This paper provides a comprehensive overview of deep learning techniques in computer vision, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. We discuss the evolution of deep learning in computer vision, highlighting key milestones and breakthroughs. Furthermore, we examine the challenges and future directions in the field, including interpretability, robustness, and scalability. Overall, this paper aims to provide a thorough understanding of the current state of deep learning for computer vision and its potential impact on various applications.
References
Tatineni, Sumanth. "Embedding AI Logic and Cyber Security into Field and Cloud Edge Gateways." International Journal of Science and Research (IJSR) 12.10 (2023): 1221-1227.
Vemori, Vamsi. "Harnessing Natural Language Processing for Context-Aware, Emotionally Intelligent Human-Vehicle Interaction: Towards Personalized User Experiences in Autonomous Vehicles." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 53-86.
Tatineni, Sumanth. "Addressing Privacy and Security Concerns Associated with the Increased Use of IoT Technologies in the US Healthcare Industry." Technix International Journal for Engineering Research (TIJER) 10.10 (2023): 523-534.
Gudala, Leeladhar, and Mahammad Shaik. "Leveraging Artificial Intelligence for Enhanced Verification: A Multi-Faceted Case Study Analysis of Best Practices and Challenges in Implementing AI-driven Zero Trust Security Models." Journal of AI-Assisted Scientific Discovery 3.2 (2023): 62-84.