3.3. Pair-Wise Cross-Attention 细粒度数据集图片数量更少,类别也少,每个类别包含的图片数量也少,并且类与类之间的差异也更细微,检测器有过拟合数据去降低难类错误率的倾向。 为了减轻这个问题我们提出了PWCA,可以被看作一个新颖的正则化方式,去正则化注意力学习。 PWCA只会用在训练中,并且会在推理中被移除以减...
However, these methods ignore the potential of the cross-attention mechanism to improve change feature discrimination and thus, may limit the final performance. Additionally, using either high-frequency-like fast change or low-frequency-like slow change alone may not effectively represent compl...
Official Pytorch implementation of Dual Cross-Attention for Medical Image Segmentation - gorkemcanates/Dual-Cross-Attention
cross encoder 用于判断图片和文本是否匹配 所使用的 [CLS] 向量是来自于建立在 text 对 image 的cross attention 的结果之上的 在dual encoder 阶段 , 这里 image encoder 的层数 是 12 层,而 text encoder 部分的层数只有 6 层,只有半个 bert base 的参数量 ,而 cross encoder 刚好又有 6 层, 参数数量...
More importantly, to achieve the optimal feature stream fusion, a CAB (cross attention block) is designed to interact the feature extracted by each branch for adaptive learning fusion. The extensive experimental comparisons on three publicly available ME benchmarks show that the proposed method ...
We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend to specific regions in images and words in text through multiple steps and gather essential information from both modalities...
In this paper, we proposed a new unsupervised learning network, DAVoxelMorph to improve the accuracy of 3D deformable medical image registration. Based on the VoxelMorph model, our network presented two modifications, one is adding a dual attention architecture, specifically, we model semantic ...
(PAL), which employs CNN with channel-wise attention and progressive learning to jointly learn a mapping from LR image to HR image. Zhang et al.14introduced a new attention-guided multi-path cross-convolution neural network (AMPCNet) that enhances the model’s learning and representation of ...
图片编码使用ViT参数初始化,文本编码使用BERT前6层初始化,多模态编码器使用BERT后六层初始化。模态交互以cross-attention的形式。 预训练: Image-Text Contrastive Learning:类似于MOCO,维护image-text pair队列(size M),分别取image和text的class token映射到256维。对于每个样本对,计算softmax-normalized image-to-tex...
基于element-wise attention 将两个domain中的common user/item作更好的利用 对于整体的算法结构,可以分为五层 Input Layer:输入层,也就是每个domain中的rating information/content information Graph Embedding Layer:利用rating和content information组成异构图,从而生成user/item的embedding,与传统的只考虑user-item intera...