While computer vision has a long history dating back to the 1960s, it has only recently reached a level of maturity where it can solve real-world problems effectively. The field has seen exponential growth in the past decade due to advancements in deep learning, availability of large datasets, and computational power. Today, computer vision powers technologies like facial recognition, autonomous driving, and augmented reality. Despite its advancements, some aspects of computer vision remain in early stages. For example, generalizing models to work in diverse environments and creating explainable AI systems for vision tasks are active areas of research. Additionally, ethical considerations, such as bias in datasets and privacy concerns, require further exploration. Overall, while computer vision is no longer in its infancy, it is still evolving as a science, with significant opportunities for innovation and discovery.
Is computer vision still in early stage as a science?

- Natural Language Processing (NLP) Basics
- Accelerated Vector Search
- Optimizing Your RAG Applications: Strategies and Methods
- Natural Language Processing (NLP) Advanced Guide
- Embedding 101
- 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
What is a knowledge graph?
A knowledge graph is a structured representation of information that illustrates the relationships between various entit
How is SSL used in recommendation systems?
SSL, or Semi-Supervised Learning, is a method that combines both labeled and unlabeled data to improve the performance o
How do you use aliases in SQL?
Aliases in SQL are temporary names assigned to tables or columns to make queries easier to read and write. They can simp