global和local的区别:whether the “attention”is placed on all source positions or on only a few source positions 今天看了下 论文 Effective Approaches to Attention-based Neural Machine Translation,里面研究了attention的两类架构:global attention 和 local attention。这里将读完论文的一些收获记录下来。论文链...
看这个论文的时候我主要是从第三小节开始看起的,也就是 attention-based models 我们基于attention机制的模型大致上可以分为广泛的两类:一类就是全局attention,一类就是局部attention。这两类的区分点在于attention是基于原始句子的全部位置,还是原始句子中一部分位置。 在这篇论文中的attention,获得解码器在t时刻真正的...
论文解读——神经网络翻译中的注意力机制 以及 global / local attention,程序员大本营,技术文章内容聚合第一站。
The Graph Attention Network (GAT) is a popular variant of GNNs known for its ability to capture complex dependencies by assigning importance weights to nodes during information aggregation. However, the GAT's reliance on local attention mechanisms limits its effectiveness in capturing global information...
local attention的q来自于输入特征,计算过程是正常的; global attention的q来自global query,在执行qk计算之前,会先对q执行repeat操作,使其形状与k和v保持一致,之后执行local attention。 stage结尾后按需求进行下采样。下采样是借助于跨步卷积实现。 对应的代码段如下: ...
局部注意力他没有写注意力公式,使用的是Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV)这篇论文里的,即基于空间+通道的注意力机制 最终它得到的fatures: MG表示全局注意力,FG表示输入到conv的特征,ML表示局部注意力,FL表示第一个bottleneck之后的...
给一张图片我们分别用cnn和local-faster cnn 抽取他们的全局特征(Gf)与局部特征(Lf)。然后用下面的公式1把它集成起来: 公式1: s.t 就是局部特征与全局特征的权重,当然这个怎么求呢。我们就用到了attention机制(来自于机器翻译里),这个机制最近用的很多啊。
Two-letter stimuli, consisting of one small letter inside a much larger one (in Experiments 1A, 1B, and 2) or inside a "blob" (in Experiment 3), were used to examine the role of size difference in global/local tasks. The small letter was placed at locations that avoided contour intera...
The neural basis of selective attention within hierarchically organized Navon figures has been extensively studied with event related potentials (ERPs), by contrasting responses obtained when attending the global and the local echelons. The findings are inherently ambiguous because both levels are always ...
attention相对RNN global attention vs local attention,这个资料很多 word2vec的处理窗口非常local,但bert等会引入更长的上下文,达到句子级别,这也是local往global的趋势 甚至召回的负采样你都可以看做往global样本空间的努力 如何在模型里头利用好local信息,但又兼顾到global的处理,应该是一个提效的理想方向。