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?
- Information Retrieval 101
- AI & Machine Learning
- Natural Language Processing (NLP) Basics
- Retrieval Augmented Generation (RAG) 101
- Master Video AI
- 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 do guardrails work in LLMs?
Guardrails in LLMs work through a combination of techniques to guide model behavior and output. These include fine-tunin
What is the best RL framework for large-scale training?
When it comes to choosing the best reinforcement learning (RL) framework for large-scale training, several options stand
How does ETL differ from ELT?
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration strategies that differ primarily