BLIP (Bootstrapping Language-Image Pre-training) is a framework for unified vision-language understanding and generation — which significantly improved performance by cleaning noisy web data using a "Captioner" and "Filter" bootstrapping cycle.

What Is BLIP?

• Definition: A VLM pre-training framework.
• Problem Solved: web image-text pairs are noisy (e.g., filenames as captions).
• Solution: "CapFilt" (Captioning and Filtering) to generate synthetic captions and filter bad ones.
• Architecture: Multimodal Mixture of Encoder-Decoder (MED).

Why BLIP Matters

• Data Quality: Proved that clean synthetic data beats noisy real data.
• Versatility: State-of-the-art on both understanding (VQA, Retrieval) and generation (Captioning).
• Open Source: The Salesforce implementation became a workhorse model for the community.

Key Components

• Image-Text Contrastive Loss (ITC): Aligns features.
• Image-Text Matching (ITM): Binary classification (match/no-match).
• Language Modeling (LM): Generates text given image.

BLIP is a masterclass in data-centric AI — demonstrating that how you curate your data is just as important as the model architecture itself.