Vision-Language Models (VLMs)
Vision-Language Models (VLMs) are neural networks that jointly process images and text, enabling tasks that require understanding both visual content and natural language. Given an image and a text prompt, a VLM can answer questions about the image, generate descriptions, locate objects by textual reference, or classify images using free-form category descriptions instead of fixed class labels.
The architecture typically pairs a vision encoder (ViT or CNN backbone) with a language model (LLM) connected through a projection layer or cross-attention mechanism. CLIP (OpenAI) aligns image and text embeddings in a shared space for zero-shot classification and retrieval. LLaVA and Qwen-VL feed visual tokens directly into a language model for open-ended visual question answering. Grounding DINO combines detection with language grounding to locate objects described by text prompts. Florence-2 and PaliGemma handle multiple vision-language tasks within a single model.
VLMs are transforming computer vision workflows. Zero-shot classification lets you define new categories by describing them in text, without collecting training data. Visual grounding enables natural language object search in images. Automated image captioning generates descriptions for accessibility and content management. For annotation workflows, VLMs can pre-label images based on text descriptions, significantly reducing manual effort. Datature has published extensive content on VLM applications and integration patterns.
