本篇CoCa: Contrastive Captioners are Image-Text Foundation Models: 1. 首先是对CoCa文章的细节精简理解 2. 然后附上原文精读 1.背景动机 针对视觉/视觉语言的任务,现可以划分为三种类型的基础模型: 1.单模态编码器—视觉/语言编码器: 解释:对单一模态(只有图像或只有文本)的输入数据进行编码的模型组件,在视...
1.3.1 image-text contrastive (ITC) loss 对于训练过程中所准备的一个batch 的 图片/文本 样本对 <x,y> 若干条, 每对样本都可以计算一个得分 s: 这里计算得分所用的向量为 image 编码器和 text 编码器 各自对应的 [CLS] 向量的乘积: 而对于 样本 x _i, 所有 y_j (j != i) 都是不匹配的负样本...
图像和文本塔经过训练后,它们可以很容易地用于零样本分类:类名称或描述用文本模型获得嵌入。然后,对于给定的图像,选择具有最接近图像嵌入的嵌入的标签。这种方法也适用于图像-文本检索。 3.2. Contrastive-tuning 对比预训练可以被视为同时学习两个任务:(1) 学习图像嵌入和 (2) 学习文本嵌入以与图像嵌入空间对齐。虽...
Contrastive learningImage-text matching aims to bridge vision and language so as to match the instance of one modality with the instance of another modality. Recent years have seen considerable progress in the research area by exploring local alignment between image regions and sentence words. ...
Jin D, Wang L, Zheng Y, Li X, Jiang F, Lin W, Pan S (July 2022) CGMN: a contrastive graph matching network for self-supervised graph similarity learning. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna...
定义损失函数:通常使用三元组损失(Triplet Loss)或对比损失(Contrastive Loss)来优化模型,使匹配的图像-文本对相似度得分高于不匹配的图像-文本对。 训练模型:使用优化器(如Adam)对模型进行迭代训练,调整模型参数以最小化损失函数。 评估模型:在测试集上评估模型的性能,常用的评估指标包括召回率(Recall@K)和平均倒数...
We do not use any image-text contrastive models for results re-ranking. ReCo achieves an FID of 5.18, compared with 6.98 when we fine-tune Stable Diffusion with COCO T2I data without regional description. ReCo also outperforms the real image retrieval baseline [45] and most prior studies ...
Another distinction is that prior works only use large-scale models pre-trained for image discriminative tasks, e.g., image classification [27, 47] or image-text contrastive learning [30, 41, 53, 57]. The con- current work MaskCLIP [15] also uses CLIP [57...
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...
ConVIRT - Contrastive Learning Representations of Images and Text pairs Pytorch implementation of the architecture descibed in the ConVIRT paper: Contrastive Learning of Medical Visual Representations from Paired Images and Text Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D. Manning, Curtis P....