While deep learning has become a dominant force in computer vision, it is not the sole approach used in the field. Deep learning models, such as convolutional neural networks (CNNs) and transformers, have revolutionized tasks like image classification, object detection, and segmentation due to their ability to learn complex patterns from large datasets. However, traditional computer vision techniques are still relevant in many scenarios. Classical methods like edge detection, feature extraction, and template matching are useful for simpler problems or when computational resources are limited. These techniques are also often combined with deep learning to create hybrid solutions. For example, feature detection methods like SIFT or ORB can be used alongside deep learning for robust visual tracking in resource-constrained environments. Deep learning has undoubtedly transformed computer vision and expanded its capabilities, but the field remains diverse. Depending on the problem at hand, a combination of classical and deep learning approaches may be the most effective solution.
Is computer vision all about deep learning now?

- Getting Started with Milvus
- Evaluating Your RAG Applications: Methods and Metrics
- Natural Language Processing (NLP) Advanced Guide
- Accelerated Vector Search
- Vector Database 101: Everything You Need to Know
- 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 can transformation rules be automated in an ETL process?
Transformation rules in ETL processes can be automated using configuration-driven tools, code-based scripting with orche
How do multimodal AI systems handle data synchronization?
Multimodal AI systems handle data synchronization by aligning various types of input data, such as text, images, and aud
How does Solr support full-text search?
Apache Solr supports full-text search through a combination of advanced indexing techniques and search functionalities t