在图像文本匹配任务中,Stacked Cross Attention 能够帮助模型理解图像中的视觉元素与文本描述之间的对应关系。例如,在图像检索任务中,给定一个文本查询,模型可以使用 Stacked Cross Attention 来关注图像中与文本描述最相关的区域,从而提高检索的准确性。同样,在图像描述生成任务中,模型可以通过 Stacked Cross Attent
然后用 Stacked Cross Attention 来推理对齐后的 image region 和 word feature 之间的 image-sentence similarity。 1.1. Stacked Cross Attention: Stacked Cross Attention 的输入有两个:一个是 image features V = {v1, v2, ... , vk},每一个图像特征编码了图像中的一个区域;另外一个是单词特征组合是 E...
Code has been made available at: (https://github.com/kuanghuei/SCAN).doi:10.1007/978-3-030-01225-0_13Kuang-Huei LeeXi ChenGang HuaHoudong HuXiaodong HeSpringer, ChamK. Lee, X. Chen, G. Hua, H. Hu, and X. He. Stacked cross attention for image-text matching. ECCV, 2018....
attention的机制,学习比较好的文本和图像表示,然后再在共享的子空间中利用hardtripletloss度量文本和图像之间的相似性。Image-TextStackedCrossAttention...距离sentencevector。Text-ImageStackedCrossAttention。采用ResNet-101的FasterR-CNN网络对每一个图像产生多个proposal ...
Stacked Cross Attention for Image-Text Matching Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, Xiaodong He 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 ...
This is Stacked Cross Attention Network, source code of Stacked Cross Attention for Image-Text Matching (project page) from Microsoft AI and Research. The paper will appear in ECCV 2018. It is built on top of the VSE++ in PyTorch. Requirements and Installation We recommended the following depe...
1. Image-Text Matching 简单来说,就是: 先对图像用Bottom-up attention(后面会解释)提取多个proposal转化为特征,再映射到和句子特征一样的维度,用bi-direction GRU对句子提取特征。 【stage 1】对每个region i 都计算所有word的attention表示 αij ,加在一起得到句子的attention表示 ait ,公式如下: 【stage 2】...
主要思路:分别对文本和图像应用attention的机制,学习比较好的文本和图像表示,然后再在共享的子空间中利用hard triplet loss度量文本和图像之间的相似性。 图像特征:采用ResNet-101的Faster R-CNN网络对每一个图像产生k个目标区域,提取每一个目标对象的特征,嵌入矩阵变换为h维的vector 文本特征:文本的每一个word得到on...
Table 2 Total number parameters for the proposed CNN with a channel attention-based model. Full size table Fig. 2 Dataset gathering and preprocessing. Full size image Preprocessing We downloaded the ADNI-1 complete 1yr 1.5T dataset from the websites (https://adni.loni.usc.edu/.) as a Neur...
features using Stacked Cross Attention. ¯Timg is theaveragetime to encode image region features extracted from region detector for one image. ¯Ttxt is theaveragetime to encode a sentence (not affected by k). ¯Ttrain is theaveragetraining timefor a mini-batch of 128 image-text pairs....