What are some applications of NLP in Computer Vision?

Natural Language Processing (NLP) and Computer Vision (CV) are often combined to create systems that understand both visual and textual data. One key application is visual question answering (VQA), where a model answers questions about an image. For example, given a picture of a street scene, a user might ask, “What color is the car?” The system uses CV to detect objects and NLP to parse the question, then combines both to generate an answer. Models like ViLBERT or LXMERT use transformer architectures to align text and visual features, enabling tasks like identifying relationships between objects or describing actions in a scene. This is useful in accessibility tools, such as helping visually impaired users interpret images, or in customer support systems that analyze product images based on user queries.

Another application is image captioning, where NLP generates descriptive text for images. For instance, a photo of a beach might produce a caption like “A sunny day with waves crashing on the shore.” This involves CV techniques like convolutional neural networks (CNNs) to extract visual features and NLP models like recurrent neural networks (RNNs) or transformers to generate coherent sentences. Tools like TensorFlow or PyTorch provide libraries for training such models. Practical uses include automated alt-text generation for websites (improving accessibility) or content moderation by flagging images with captions that violate guidelines. Metrics like BLEU or CIDEr are often used to evaluate caption quality, ensuring outputs align with human expectations.

A third application is multimodal search, where users query databases using both text and images. For example, searching “shoes similar to this image but in blue” combines a photo of a shoe with a text modifier. CLIP (Contrastive Language-Image Pretraining) by OpenAI is a prominent model here, embedding images and text into a shared space for retrieval. Developers can implement this using APIs or frameworks like Hugging Face Transformers. Use cases include e-commerce platforms (finding products based on visual and textual criteria) or media archives (locating videos using scene descriptions). This approach improves search accuracy by leveraging context from both modalities, reducing reliance on manual tagging or metadata alone.