
BLIP - Hugging Face
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 …
GitHub - salesforce/BLIP: PyTorch code for BLIP: Bootstrapping …
Announcement: BLIP is now officially integrated into LAVIS - a one-stop library for language-and-vision research and applications! This is the PyTorch code of the BLIP paper [blog]. The code has been tested on PyTorch 1.10. To install the dependencies, run. Catalog: Run our interactive demo using Colab notebook (no GPU needed).
BLIP:统一视觉语言理解与生成的预训练模型 - CSDN博客
2023年12月25日 · blip是一种基于vlp的新框架,统一并灵活地应用于视觉-语言理解任务和生成任务。blip通过引导生成图像描述来有效利用噪声网络数据,从而在多个下游任务上取得了最先进的性能。
[2201.12086] BLIP: Bootstrapping Language-Image Pre-training …
2022年1月28日 · 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.
Salesforce/blip-image-captioning-large · Hugging Face
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 …
一文读懂BLIP和BLIP-2多模态预训练 - 知乎 - 知乎专栏
BLIP (Bootstrapping Language-Image Pretraining)是 salesforce 在2022年提出的多模态框架,是理解和生成的统一,引入了跨模态的编码器和解码器,实现了跨模态信息流动,在多项视觉和语言任务取得SOTA。 在AIGC中通常用来给图像生成prompt,好的prompt对交叉注意力的微调非常关键,例如ControlNet中的Automatic Prompt就是BLIP生成的。 为什么叫Bootstrapping,是因为训练数据来自网络图文对,包含大量噪声,所以 增加了一个在线数据打标签和清理的任务,把处 …
Salesforce/blip2-opt-2.7b · Hugging Face
BLIP-2, OPT-2.7b, pre-trained only BLIP-2 model, leveraging OPT-2.7b (a large language model with 2.7 billion parameters). It was introduced in the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Li et al. and first released in this repository .
BLIP核心模块解读 - 知乎 - 知乎专栏
BLIP 的模型结构看上图,会涉及4个结构(Image-grounded Text Decoder 、 Image-grounded Text Encoder 、Image-grounded Text Decoder)和3种损失(ITC 、 ITM 、LM)。 Image Encoder (ViT) :首先进行图像特征的提取; Text Encoder (BERT) :这是一个标准的 BERT,提取文本的特征; Image-grounded Text Encoder (变种 BERT):在标准 BERT 的结构里,于 Bi Self-Att 和 Feed Forward 之间插入 Cross Attention 模块,以引入视觉特征;
BLIP2模型:图像到文本生成的预训练论文解析与测试-CSDN博客
2023年6月4日 · 本文为《深入浅出多模态》系列多模态经典模型blip,首先从整体介绍多模态模型发展,对其中经典blip模型进行详述,从具体论文、数据集、代码、模型结构、结果等角度分析,本专栏适合从事多模态小白及爱好者学习,欢迎大家关注,如有侵权请联系删除!
[2301.12597] BLIP-2: Bootstrapping Language-Image Pre-training …
2023年1月30日 · This paper proposes BLIP-2, a generic and efficient pre-training 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 pre-trained in two stages.
- 某些结果已被删除