Stacked Cross Attention 是一种注意力机制,它在处理多模态数据(如图像和文本)时,能够捕捉不同模态间的交互信息。这种机制通过在多个层级上堆叠注意力模块,逐步深化对跨模态信息的理解和融合。每个注意力模块都会根据前一层的输出,重新计算不同模态元素之间的相关性权重,从而实现对关键信息的聚焦。 2. 阐述Stacked Cro...
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....
《Stacked Cross Attention for Image-Text Matching》 ECCV 2018 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...
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 ...
Stacked Cross Attention for Image-Text Matching: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IV 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, ...
License Apache License 2.0 Acknowledgments The authors would like to thank Po-Sen Huang and Yokesh Kumar for helping the manuscript. We also thank Li Huang, Arun Sacheti, and Bing Multimedia team for supporting this work.About PyTorch source code for "Stacked Cross Attention for Image-Text Matc...
《Stacked Cross Attention》总结 ECCV 2018 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...
This is Stacked Cross Attention Network, source code ofStacked 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 theVSE++in PyTorch. Requirements and Installation ...
具体:word ---> one-hot vector ---> embeding到300维 ---> 双向GRU到h维 5. 总结 这篇文章最突出的就在于把attention应用到了word和region层面上的对齐,这就带来了很大解释性方面的提升,这样word和region的互相注意力机制和相似度计算也是题目叫做 Stacked Cross Attention(叠加交叉)的原因。