Introduction

The BLIP model is trained to generate a caption based on the content of an image. The model completes this task using a novel ML technique known as Vision-Language Pre-training (VLP). The BLIP model stands out from other VLP architectures as it excels in both understanding and generation tasks.

This model's output is a string of text containing the generated caption.

BLIP – Image Captioner

This model's intended use is generating metadata for images, resulting in improved SEO. Additionally, automatic image captioning will reduce human workload and reduce subjectivity.

BLIP is not well suited for domain-specific images, such as medical images, and it may not generate accurate captions.

Pro Tip

For captions in other languages, feed the output of this model into a translation model!

Salesforce – BLIP

More info:

• Original repository: GitHub
• Interactive Demo: Google Colab

Paper

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

Authors: Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi

Abstract

Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, 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. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.