VLM (Vision-Language Model) refers to a class of AI systems that process and understand both visual and textual information. These models align images with corresponding text. This enables tasks like image captioning, visual question answering, and multimodal reasoning. VLMs support AI applications that need understanding across different data types.
Key Characteristics of VLMs
Multimodal Training: Trains on datasets with image-text pairs to learn visual and language links.
Cross-Attention Mechanisms: Connect image regions with text tokens.
Pretrained Backbones: Use vision encoders like CLIP, ViT, or CNNs with language models.
Zero-Shot Capabilities: Handle new tasks by interpreting unseen image-text combinations.
Multilingual and Multi-domain Use: Work across languages and specialized domains.
Applications of VLMs
Image Captioning: Creates captions for photos or illustrations.
Visual Question Answering (VQA): Responds to questions using image content.
Multimodal Search: Locates images or documents from text input.
Accessibility Tools: Aids users with visual impairments by describing content.
Creative Generation: Helps in comic creation and interactive storytelling.
Why VLMs Matter
VLMs connect vision and language to support tasks across data types. They pair images with text, making them useful in systems that need context-aware understanding.
