Computer vision faces challenges with data dependency. Many models require large, high-quality datasets for training, which may not always be available or diverse enough to handle real-world scenarios. Bias in datasets can lead to poor performance in identifying underrepresented groups or objects. Another limitation is computational cost. Training and deploying computer vision models, especially deep learning-based ones, demand significant computational power and storage. This can limit accessibility for smaller organizations or resource-constrained devices like edge systems. Generalization remains a hurdle. Models often struggle when exposed to environments or conditions different from their training data. For instance, an object detection model trained in sunny weather may fail in foggy conditions, posing challenges for applications like autonomous driving.
What are the current major limitations of computer vision?

- How to Pick the Right Vector Database for Your Use Case
- Getting Started with Milvus
- Natural Language Processing (NLP) Basics
- Embedding 101
- Optimizing Your RAG Applications: Strategies and Methods
- 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 handle multimodal data?
Deep learning effectively handles multimodal data—data that comes from various sources, such as text, images, audio, and
What specific challenges do extremely large datasets (say, hundreds of millions or billions of vectors) introduce to vector search that might not appear at smaller scale?
Handling extremely large datasets in vector search introduces challenges that become critical at scale but are less appa
How can machine learning benefit image recognition?
Machine learning benefits image recognition by enabling automatic feature extraction and improving accuracy in identifyi