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?

- The Definitive Guide to Building RAG Apps with LlamaIndex
- Natural Language Processing (NLP) Advanced Guide
- Optimizing Your RAG Applications: Strategies and Methods
- Evaluating Your RAG Applications: Methods and Metrics
- Getting Started with Zilliz Cloud
- 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 deep learning improve speech recognition?
Deep learning improves speech recognition by using neural networks to process and understand audio data more effectively
How do you integrate AR features into existing mobile applications?
Integrating augmented reality (AR) features into existing mobile applications involves several structured steps. First,
Are guardrails compatible with multimodal LLMs?
Yes, guardrails are compatible with multimodal LLMs, which are designed to handle multiple types of input and output, su