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# BLIP
## Overview
BLIP モデルは、[BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://huggingface.co/papers/2201.12086) で Junnan Li、Dongxu Li、Caiming Xiong、Steven Hoi によって提案されました。 。
BLIP は、次のようなさまざまなマルチモーダル タスクを実行できるモデルです。
- 視覚的な質問応答
- 画像とテキストの検索(画像とテキストのマッチング)
- 画像キャプション
論文の要約は次のとおりです。
*視覚言語事前トレーニング (VLP) により、多くの視覚言語タスクのパフォーマンスが向上しました。
ただし、既存の事前トレーニング済みモデルのほとんどは、理解ベースのタスクまたは世代ベースのタスクのいずれかでのみ優れています。さらに、最適ではない監視ソースである Web から収集されたノイズの多い画像とテキストのペアを使用してデータセットをスケールアップすることで、パフォーマンスの向上が大幅に達成されました。この論文では、視覚言語の理解と生成タスクの両方に柔軟に移行する新しい VLP フレームワークである BLIP を提案します。 BLIP は、キャプションをブートストラップすることでノイズの多い Web データを効果的に利用します。キャプショナーが合成キャプションを生成し、フィルターがノイズの多いキャプションを除去します。画像テキスト検索 (平均再現率 +2.7%@1)、画像キャプション作成 (CIDEr で +2.8%)、VQA ( VQA スコアは +1.6%)。 BLIP は、ゼロショット方式でビデオ言語タスクに直接転送した場合にも、強力な一般化能力を発揮します。コード、モデル、データセットがリリースされています。*
![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif)
このモデルは [ybelkada](https://huggingface.co/ybelkada) によって提供されました。
元のコードは [ここ](https://github.com/salesforce/BLIP) にあります。
## Resources
- [Jupyter ノートブック](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) カスタム データセットの画像キャプション用に BLIP を微調整する方法
## BlipConfig
[[autodoc]] BlipConfig
## BlipTextConfig
[[autodoc]] BlipTextConfig
## BlipVisionConfig
[[autodoc]] BlipVisionConfig
## BlipProcessor
[[autodoc]] BlipProcessor
## BlipImageProcessor
[[autodoc]] BlipImageProcessor
- preprocess
## BlipImageProcessorFast
[[autodoc]] BlipImageProcessorFast
- preprocess
## BlipModel
[[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