BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner.

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• 1 However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks.
• 2 Additionally, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision.
• 3 In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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