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...
Image-text retrievalMulti-subspace learningCross-modal matchingJoint representation learning has been an attractive way to solve image-text retrieval problem due to its efficiency on both time and storage. On the one hand, the most classical methods model the joint semantic subspace with respect to ...
MatchPyramid主要分三步: 第一步:构建Matching Matrix 我们将文本匹配问题的输入表示成一个匹配矩阵M,矩阵的每一个元素Mij代表两个句子之间基本的交互关系,比如第一个句子的第i个单词wi和第二个句子的第j个单词vj之间的相似度。我们使用⊗代表获得相似值的一般操作。则: 因此,我们也可以将矩阵M看成一个图片,里面...
The key challenge in image-text matching lies in learning thecorrespondenceof image and text, such that can reflect thesimilarityof image-text pairs accurately. 现有的方法: ①:one-to-one approaches One-to-one approaches learnthe correspondence between the whole image and textwithout external object...
March 2018 arXiv preprint arXiv:1803.08024 Publication Download BibTex In this paper, we study the problem of image-text matching. Inferring the latent semantic alignment between objects or other salient stuffs (e.g. snow, sky, lawn) and the corresponding words in sentences allows to capture fi...
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...
Abstract: The hubness problem widely exists in high-dimensional embedding space and is a fundamental source of error for cross-modal matching tasks. In this work, we study the emergence of hubs in Visual Semantic Embeddings (VSE) with application to text-image matching. We analyze the pros and...
MatchPyramid来自Liang Pang等在2016发表的一篇文章Text Matching as Image Recognition,大意为利用图像识别的方式进行文本匹配。 二、思路 对于文本匹配,基本思路如下述公式: 其中T为文本,函数θθθ代表将文本转换为对应的表示,函数FFF则代表两个文本表示之间的交互关系。 由侧重点不同可分为表示方法与交互方法,即注重...
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...