将它们以对比学习的方式加入到loss中,得到Intra-Modal Contrastive loss: L_{imc}=\sum_{(I, T)\in B}-\text{log}\frac{1}{\sum_{T_k\in T_{hn}}\text{exp}^{S(T, T_k)}} 2.2.2 Cross-Modal Rank with Adaptive Threshold loss,CMR 旨在让模型学习细粒度的图像-文本跨模态对齐能力。此...
In this paper, we propose an improving text-image cross-modal retrieval framework with contrastive loss, which considers multiple texts of one image. Using the overall text features, our approach makes better alignment between image and its corresponding text center. Results on ...
4.4 Cross-Modal Contrastive Loss 4.5 Inference for Instance-Level Retrieval 5 Result 0 总览 场景:在细粒度产品类别之间执行弱监督多模态实例级产品检索;电商多模态 本文贡献: 发布数据集:Product1M,这是用于真实世界实例级检索的最大的多模态化妆品数据集之一 提出了一个新的模型,名为跨模态对比Transformer,用于...
Furthermore, the proposed method outperforms the recent CBIR successfully used in medical image retrieval IMTDF, and the recent cross-modal image retrieval method TC-Net (Table 3). Similarly to the representation learning of CoMIRs used in our method, TC-NET uses a contrastive loss (triplet lo...
We present the Cross-Modal Contrastive Generative Adversarial Network (XMC-GAN) in “Cross-Modal Contrastive Learning for Text-to-Image Generation,” which addresses text-to-image generation by learning to maximize the similarity matrix between text and image using intermodal (image-text) and intra...
Cross-Modal Center Loss for 3D Cross-Modal Retrieval Longlong Jing∗ Elahe Vahdani∗ Jiaxing Tan Yingli Tian The City University of New York Abstract Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing...
Enriched Music Representations With Multiple Cross-Modal Contrastive Learning 作者: Ferraro, Andres;Drossos, Konstantinos;Kim, Yuntae;Bogdanov, Dmitry;Favory, Xavier;摘要: Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of ...
为了训练和评估Cross-modal Graph Matching Network模型,你需要: 准备数据集:选择适合图像-文本检索任务的数据集,如Flickr30k或MSCOCO。 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...
作者认为交叉熵函数对噪声样本,也就是p接近0的样本太过敏感,导致大部分loss是由这种样本产生了,进而使得噪声样本主导了参数的更新方向,于是作者提出橙色曲线的函数作为改进,即RC loss,这种函数对噪声样本并不敏感,可以使得参数朝着更加正确的方向更新。 2.2 Multimodal Contrastive loss (MC) L_c MC缩小不同模态之间...
Cross-Modal Contrastive Learning 图文匹配的任务,一般都采用对比学习的方法(让paired的image-text在特征空间中越近越好,而non-paired则越远越好)。 Contrastive Learning Loss 具体优化的目标为如下。有3种类型的数据,我们希望拉近正样本的距离而拉远负样本距离。除了初始匹配的有标签的pair作为正样本外,还利用retrieval...