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Methods › Computer Vision › Vision and Language Pre-Trained Models › BLIP

BLIP: Bootstrapping Language-Image Pre-training

BLIP

93 papers tagged archive 2025-07-28

Introduced by Junnan Li et al. in BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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 video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP.

PaperSource

Papers archive 2025-07-28

30 shown of 93, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 123 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Retrieval22
Image Captioning19
Question Answering14
Visual Question Answering13
Language Modelling12
Image Generation9
Text Retrieval9
Visual Question Answering (VQA)9
Decoder7
Image-text Retrieval7
Language Modeling7
Large Language Model6
Attribute5
Cross-Modal Retrieval5
Image Retrieval5
Object Detection5
object-detection5
Diversity4
Image Classification4
Image-text matching4

Usage over time archive 2025-07-28

Papers per year tagged with BLIP: 2022 to 2025, peak 42 42 0 2022: 7 papers 2022 2023: 31 papers 2023 2024: 42 papers 2024 2025: 13 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (93 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Vision and Language Pre-Trained Models

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