Where is the difference between NLP and computer vision?

Natural Language Processing (NLP) and Computer Vision (CV) are distinct subfields of artificial intelligence that focus on different types of data and use separate technical approaches. NLP deals with understanding and generating human language, while Computer Vision processes and interprets visual data like images or videos. The core difference lies in their input data: NLP works with text (words, sentences), and CV handles pixels, shapes, and spatial relationships. For example, an NLP model might analyze sentiment in a tweet, while a CV system could identify objects in a photo.

The technical methods used in each field also diverge. NLP relies heavily on techniques like tokenization (breaking text into words or subwords), embeddings (mapping words to numerical vectors), and attention mechanisms (identifying important context). Models such as BERT or GPT process sequential data, often using transformers to handle long-range dependencies in language. In contrast, Computer Vision employs convolutional neural networks (CNNs) to detect patterns in grid-like pixel data. For instance, a CNN might use filters to recognize edges in an image before identifying higher-level features like faces. While transformers have recently been adapted for CV (e.g., Vision Transformers), the spatial hierarchy of visual data remains a key focus.

Applications and challenges also highlight the differences. NLP powers chatbots, translation services (e.g., Google Translate), and text summarization, but struggles with ambiguity, sarcasm, or low-resource languages. Computer Vision enables facial recognition, medical imaging analysis, and self-driving car navigation, but faces issues like occlusion (objects blocking each other) or varying lighting conditions. A developer working on NLP might use libraries like spaCy or Hugging Face Transformers, while a CV engineer could leverage OpenCV or PyTorch with CNN architectures like ResNet. Both fields require domain-specific preprocessing: NLP handles stopword removal and lemmatization, while CV normalizes pixel values and applies data augmentation like rotation or cropping.