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?
- Mastering Audio AI
- How to Pick the Right Vector Database for Your Use Case
- Embedding 101
- Evaluating Your RAG Applications: Methods and Metrics
- Natural Language Processing (NLP) Advanced Guide
- 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 AI visual inspection for defect detection?
AI visual inspection for defect detection refers to using artificial intelligence, particularly computer vision and mach
How do recommender systems work?
Recommender systems are tools that help users discover items they may find intriguing based on their preferences and beh
How does few-shot learning work with reinforcement learning environments?
Few-shot learning in reinforcement learning (RL) environments allows models to quickly adapt to new tasks or conditions