Computer vision problems are complex due to the variability and ambiguity in visual data. Factors like lighting, occlusion, perspective distortion, and background clutter can significantly impact the performance of vision algorithms. Extracting meaningful features from high-dimensional data and ensuring robustness across diverse conditions require advanced techniques, such as deep learning. Moreover, the computational demands of processing large datasets and training sophisticated models add to the complexity. These challenges make computer vision a highly interdisciplinary field requiring expertise in algorithms, data processing, and hardware optimization.
Why are computer vision problems complex to solve?

- Master Video AI
- GenAI Ecosystem
- Evaluating Your RAG Applications: Methods and Metrics
- Vector Database 101: Everything You Need to Know
- Accelerated Vector Search
- 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 zero-shot learning in NLP?
Zero-shot learning in NLP refers to the ability of a model to perform tasks it has not been explicitly trained on. This
What is an example of zero-shot learning in action?
Zero-shot learning (ZSL) is a machine learning approach where a model learns to recognize objects, categories, or tasks
What is Keras, and how does it relate to TensorFlow?
Keras is an open-source high-level neural network API written in Python that runs on top of other deep learning framewor