Theattentionfunction calculates the context vector and the attention weights using Bahdanau attention. Get function[contextVector, attentionWeights] = attention(hidden,features,weights1,...bias1,weights2,bias2,weightsV,biasV)% Model dimensions.[embeddingDimension,numFeatures,miniBatchSize] = size(feature...
Adaptive attention mechanismConsidering the image captioning problem, it is difficult to correctly extract the global features of the images. At the same time, most attention methods force each word to correspond to the image region, ignoring the phenomenon that words such as "the" in the ...
Spatial Attention机制通过动态调整注意力焦点,使模型能够更好地适应不同数据集的特点。 实践案例 在实际应用中,许多研究已经证明了Spatial Attention在Image Captioning任务中的有效性。例如,一些研究提出了结合CNN(卷积神经网络)和LSTM(长短期记忆网络)的encoder-decoder框架,并在其中引入了Spatial Attention机制。这些模型...
2. Image Captioning with Attention 1) 我们的CNN 不输出一个 single vector, 而是生成一个 grid of vectors,可以让每个vector对应一个图片中的特殊地方 2) RNN的每一步timestep中,除了在每一步中采样,它也产生了一个分布(distribution)对应于图片中它想注意的位置 a1,a2,... 3) 对于 soft attention 采用...
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering 摘要: 从上到下的视觉注意力机制已经成功的应用在caption模型中,使得模型可以更深层次地对图像进行理解。本文中,我们提出一种方法结合从上到下的以及从下到上的注意力机制,能够关注到图像中的具体目标以及一些显著的区域。简单说...
ImageCaptioningwithSemanticAttention使用attention将图像的CNN feature和Attributedetector检测到的属性词综合到一起作为...imagefeature从卷积层中提取针对不同spatial position的feature,是一个向量集合而不是单个向量 lstm的初始状态通过MLP学习得到Attention机制分为hard和 ...
文中,作者一共实验了三种注意力机制,分别为additive attention、stochastic hard attention和deterministic soft attention。 2.2.1 additive attention 注意力权重的计算方式为: e_{ti}=f_{att}(a_i,h_{t-1})\alpha_{ti}=\frac{exp(e_{ti})}{\sum^L_{k=1}exp(e_{tk})}\hat{z}_t=\phi({a_i...
[25]. To make mandatory correspondence between descriptive text words and image regions effective, Deng et al. proposed a Dense network and adaptive attention technique [26]. A multitask learning method through a dual learning mechanism for cross-domain image captioning is proposed in [27]. It ...
在本文中,作者提出了一个“Attention on Attention”(AoA)模块,它扩展了传统的注意机制来确定注意结果和查询之间的相关性。 Method Attention on Attention 注意模块fatt(Q、K、V)对一些查询、键和值进行操作,并生成一些加权平均向量(分别用Q、K、V和Vˆ表示)。它首先测量Q和K之间的相似性,然后使用相似性得分来...
Considering the image captioning problem, it is difficult to correctly extract the global features of the images. At the same time, most attention methods force each word to correspond to the image region, ignoring the phenomenon that words such as “the” in the description text cannot correspon...