1.图像-文本对比损失(Image-Text Contrastive Loss,ITC): 图像-文本对比损失(ITC)主要用于ViT和BERT的组合。其目标是使正样本图像-文本对的相似度更大,负样本图像-文本对相似度更低。 BLIP沿用了ALBEF的 ITC 损失法,即引入动量编码器来生成特征,并从动量编码器中创建软标签作为训练目标,以考虑负对中潜在的正标...
Image-Text Alignment:用了BLIP 中 Encoder 结构,image embedding 与 text embeding送入encoder。用粗粒度的 Image-Text Contrastive(ITC) Loss 和细粒度的 Image-Text Matching(ITM) Loss 分别进行监督。 训练目标: Image tagging:对每个类别进行非对称交叉熵损失 Image-Tag-Text generation:语言建模损失 (LM) ,以...
文本处理:SD采用OpenAI的CLIP(Contrastive Language-Image Pre-Training语言图片对比学习预训练模型)进行文字到图片的处理,具体使用的是clip-vit-large-patch14。对于输入text,送入CLIP text encoder后得到最后的hidden states,其特征维度大小为77x768(77是token的数量),这个细粒度的text embeddings将以cross attention的方...
To this end, we propose an Instance Contrastive Embedding (IConE) method for image–text cross-modal retrieval. Specifically, we first transfer the multi-modal pre-training model to the cross-modal retrieval task to leverage the interactive information between image and text, thereby enhancing the...
CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities...
2021 年 OpenAI 发表的论文《Learning Transferable Visual Models From Natural Language Supervision》提出了 CLIP (Contrastive Language-Image Pre-training) 模型,并在论文中详细阐述了如何通过自然语言处理监督信号,来训练可迁移的视觉模型(其原理架构如下图所示)。
· u*v.T# Bidirectional contrastive loss6i2t=SoftCE(logits,target)7t2i=SoftCE(logits.T,target.T)8loss=(i2t+t2i)/29loss.backward()# The Target Modification function10defTargetM(y):# Note y = 0 for image-text in loader11cap m=(y==0).sum()12cls m=y[y>0].max()13y[y==0]...
要让AI 在图像编辑时”听懂”文字引导,典型方法是利用对比图文预训练(Contrastive Language-Image Pre-Training,CLIP)模型。CLIP 模型可以将文字和图像编码到可比较的隐空间中,并给出”图像是否符合文字描述”的跨模态相似度信息,从而建立起文字和图像之间的语义联系。 但仅使用 CLIP 模型很难直接对于图像编辑进行有效...
and more coherent semantic space. We fully decouple the image and text encoder. In many previous unified encoder-decoder models [7, 35, 85], the image and text are fused in the encoder side. This design makes it intractable not only for global image-text contrastive learning [64, 84], ...
PIMA - A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning deep-learning graph-neural-networks text-image-retrieval Updated Nov 24, 2022 Jupyter Notebook MayssaJaz / Text2Image-Search Star 1 Code Issues Pull requests A search engine, operating on the ...