本篇文章主要结合这些 VQA 模型和我的实验结果,写一写我对 image-text matching 这个 task 的想法。 VQA 和 image-text matching 的问题有很多共同点,比如两者都分别接受 image 和 text 特征然后进行 encode。如果把 matching 看作二分类问题,那不同点几乎就只有 VQA 的输出是多类,而 matching 是两类了。所以...
论文链接:Negative-Aware Attention Framework for Image-Text Matching(基于负感知注意力的图文匹配,CVPR2022) 代码主页:https://github.com/CrossmodalGroup/NAAF 主要优势 (Highlights): 1)不额外添加任何学习参数前提下,在基础基线SCAN上取得显著性能提升,达到SOTA; 2)模型设计简单有效,只需要SCAN 的文本-图像(Text...
Text-image matching has been one of the most popular ones among them. Most methods involve two phases: 1) training: two neural networks (one image encoder and one text encoder) are learned end-to-end, mapping texts and images into a joint space, where vectors (either texts or images) wi...
Visual Semantic Reasoning for Image-Text Matching(ICCV 2019)【VSRN模型采用GCN对图像区域的关系进行了推理,生成局部的具有语义关系信息的特征。然后再基于局部的结果做全局推理,过滤不重要的信息,最后得到图像表征。它在训练阶段同时进行了图像描述生成和图文匹配任务,更好地理解和对齐视觉和文本的语义信息。】 Focus...
和本文的通用表示,作者为上面两步得到的多模态特征进行自监督预训练,与之前只mask掉文本token的方法不同,作者在这里将图像块和文本token同时mask,设计了三个独立的自监督任务:(1)Masked语言建模(Masked Language Modeling,MLM),(2)Masked图像建模(Masked Image Modeling,MIM),(3)图像本文匹配(Image-Text Matching,...
在模型预训练过程中，设计了四个任务来对语言信息和视觉内容以及它们之间的交互进行建模。四个任务分别为：掩码语言建模（Masked Language Modeling）、掩码对象分类（Masked Object Classification）、掩码区域特征回归（Masked Region Feature Regression）、图文匹配（Image-Text Matching）。掩码语言建模简称MLM，在这个任务...
In this paper, we study the problem of image-text matching. Inferring the latent semantic alignment between objects or other salient stuff (e.g. snow, sky, lawn) and the corresponding words in sentences allows to capture fine-grained interplay between vision and language, and makes image-text...
Image-text matching plays a central role in bridging the semantic gap between vision and language. The key point to achieve precise visual-semantic alignment lies in capturing the fine-grained cross-modal correspondence between image and text. Most previous methods rely on single-step reasoning to ...
作者设计了三个预训练任务:掩码语言建模 (Masked Language Modelin,MLM)、图像文本匹配 (Image-Text Matching,ITM) 和掩码区域建模 (Masked Region Modeling, MRM)。不同于在多模态预训练的并发工作-将联合随机掩码应用于两种模态的训练,作者在预训练任务上使用了条件掩码。综合分析表明,条件掩码比非条件掩码产生更好...
This isNegative-Aware Attention Framework for Image-Text Matching, source code of NAAF. The paper is accepted by CVPR2022.Download Paper. Its Chinese blog can be foundhere. It is built on top of theSCANin PyTorch. Our series of work based on optimal discriminative learning is published in ...