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* toctree

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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

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---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* "to be not" -> "not to be" (#32636)

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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>

2025-03-03 10:33:46 -08:00

4.5 KiB

Raw Blame History

BLIP

Overview

The BLIP model was proposed in BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.

BLIP is a model that is able to perform various multi-modal tasks including:

Visual Question Answering
Image-Text retrieval (Image-text matching)
Image Captioning

The abstract from the paper is the following:

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.

This model was contributed by ybelkada. The original code can be found here.

Resources

Jupyter notebook on how to fine-tune BLIP for image captioning on a custom dataset

BlipConfig

autodoc BlipConfig - from_text_vision_configs

BlipTextConfig

autodoc BlipTextConfig

BlipVisionConfig

autodoc BlipVisionConfig

BlipProcessor

autodoc BlipProcessor

BlipImageProcessor

autodoc BlipImageProcessor - preprocess

BlipImageProcessorFast

autodoc BlipImageProcessorFast - preprocess

BlipModel

BlipModel is going to be deprecated in future versions, please use BlipForConditionalGeneration, BlipForImageTextRetrieval or BlipForQuestionAnswering depending on your usecase.

autodoc BlipModel - forward - get_text_features - get_image_features

BlipTextModel

autodoc BlipTextModel - forward

BlipVisionModel

autodoc BlipVisionModel - forward

BlipForConditionalGeneration

autodoc BlipForConditionalGeneration - forward

BlipForImageTextRetrieval

autodoc BlipForImageTextRetrieval - forward

BlipForQuestionAnswering

autodoc BlipForQuestionAnswering - forward

TFBlipModel

autodoc TFBlipModel - call - get_text_features - get_image_features

TFBlipTextModel

autodoc TFBlipTextModel - call

TFBlipVisionModel

autodoc TFBlipVisionModel - call

TFBlipForConditionalGeneration

autodoc TFBlipForConditionalGeneration - call

TFBlipForImageTextRetrieval

autodoc TFBlipForImageTextRetrieval - call

TFBlipForQuestionAnswering

autodoc TFBlipForQuestionAnswering - call

4.5 KiB Raw Blame History