Abstract: Deep learning models in computer vision face challenges such as high computational resource demands and limited generalization in practical scenarios. To address these issues, this study ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

Labeling images is a costly and slow process in many computer vision projects. It often introduces bias and reduces the ability to scale large datasets. Therefore, researchers have been looking for ...

In this tutorial, we explore advanced computer vision techniques using TorchVision’s v2 transforms, modern augmentation strategies, and powerful training enhancements. We walk through the process of ...

Computer vision moved fast in 2025: new multimodal backbones, larger open datasets, and tighter model–systems integration. Practitioners need sources that publish rigorously, link code and benchmarks, ...

Deep learning is an AI function and a subset of machine learning, used for processing large amounts of complex data. Deep learning can automatically create algorithms based on data patterns.

Abstract: The rapid advancement of AI, particularly in computer vision and deep learning, has revolutionized industrial automation. This paper presents an AI-driven system that integrates computer ...

Computer vision continues to be one of the most dynamic and impactful fields in artificial intelligence. Thanks to breakthroughs in deep learning, architecture design and data efficiency, machines are ...