Vision processing in AI involves analyzing and interpreting visual data, such as images and videos, to extract meaningful information. This process typically includes tasks like image preprocessing, feature extraction, and applying machine learning models for tasks like classification, segmentation, or object detection. Vision processing is integral to applications like facial recognition, autonomous vehicles, and augmented reality. Techniques such as convolutional neural networks (CNNs) and transformers are commonly used for vision processing in modern AI systems, enabling them to handle large-scale and complex visual data.
What is vision processing in AI?

- Optimizing Your RAG Applications: Strategies and Methods
- Natural Language Processing (NLP) Advanced Guide
- Information Retrieval 101
- Evaluating Your RAG Applications: Methods and Metrics
- The Definitive Guide to Building RAG Apps with LlamaIndex
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does AutoML support model versioning?
AutoML, or Automated Machine Learning, supports model versioning by providing tools and frameworks that help track, mana
What is the procedure to use a Sentence Transformer model in a zero-shot or few-shot learning scenario for a specific task?
To use a Sentence Transformer model in zero-shot or few-shot learning, you first leverage its ability to generate semant
How do auto-augment policies work?
Auto-augment policies are techniques used in machine learning to enhance datasets through automated augmentation methods