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BLIP-2

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Abstract

The cost of vision-and-language pre-training has become increasingly prohibitive due to end-toend training of large-scale models. This paper proposes BLIP-2, a generic and efficient pretraining strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pretrained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various visionlanguage tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model’s emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.

How to use it?

Use the model

from mmpretrain import inference_model

result = inference_model('blip2-opt2.7b_3rdparty-zeroshot_caption', 'demo/cat-dog.png')
print(result)
# {'pred_caption': 'a dog and a cat sitting on a blanket'}

Test Command

Prepare your dataset according to the docs.

Test:

python tools/test.py configs/blip2/blip2_8xb32_retrieval.py https://download.openmmlab.com/mmclassification/v1/blip2/blip2_3rdparty_pretrain_20230505-f7ef4390.pth

Models and results

Image Caption on COCO

Model Params (M) BLEU-4 CIDER Config Download
blip2-opt2.7b_3rdparty-zeroshot_caption* 3770.47 32.90 111.10 config model

Visual Question Answering on VQAv2

Model Params (M) Accuracy Config Download
blip2-opt2.7b_3rdparty-zeroshot_vqa* 3770.47 53.50 config model

Image-To-Text Retrieval on COCO

Model Params (M) Recall@1 Config Download
blip2_3rdparty_retrieval* 1173.19 85.40 config model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven't reproduce the training results.

Citation

@article{beitv2,
    title={Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models},
    author={Li, Junnan and Li, Dongxu and Savarese, Silvio and Hoi, Steven},
    year={2023},
    eprint={2301.12597},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}