Blip 2 Vision Language

BLIP-2: Vision-Language Pre-training

Comprehensive guide to using Salesforce's BLIP-2 for vision-language tasks with frozen image encoders and large language models.

When to use BLIP-2

Use BLIP-2 when:

• Need high-quality image captioning with natural descriptions
• Building visual question answering (VQA) systems
• Require zero-shot image-text understanding without task-specific training
• Want to leverage LLM reasoning for visual tasks
• Building multimodal conversational AI
• Need image-text retrieval or matching

Key features:

• Q-Former architecture: Lightweight query transformer bridges vision and language
• Frozen backbone efficiency: No need to fine-tune large vision/language models
• Multiple LLM backends: OPT (2.7B, 6.7B) and FlanT5 (XL, XXL)
• Zero-shot capabilities: Strong performance without task-specific training
• Efficient training: Only trains Q-Former (~188M parameters)
• State-of-the-art results: Beats larger models on VQA benchmarks