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...
Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature Integration - ziyueqingwan/LocalGlobalAttention
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。
局部attention 作者在论文中说,自己提出来全局attention和局部attention是来源于soft和hard attention,soft和hard attention是从图片领域扩展过来的概念。 局部attention原理是把注意力放在一个小窗口内的句子内容,而不是全部内容。这个局部内容是这样获取的。首先模型为每一个目标时刻单词产生一个对齐位置ptpt,然后我们找到...
AttentionDeep networkHuman emotion recognition is an active research area in artificial intelligence and has made substantial progress over the past few years. Many recent works mainly focus on facial regions to infer human affection, while the surrounding context information is not effectively utilized....
Liu, “Expectation-maximization attention networks for semantic segmentation”, in Proc. 23rd Int. Conf. Computer Vision, 2019.. Google Scholar [14] G. Xu, X. Wang, and X. Xu. Single image enhancement in sandstorm weather via tensor least square. IEEE/CAA J. Autom. Sinica , 2020 ,...
🚀 The feature, motivation and pitch Gemma-2 and new Ministral models use alternating sliding window and full attention layers to reduce the size of the KV cache. The KV cache is a huge inference bottleneck and this technique could be fin...
To exploit the heterogeneous features from both imaging and non-imaging data, we propose a Local-Global Co-attention neural network (LGC-Net), which features two specialized blocks: the Paired Local-Global Attention (PLGA) Block and the Paired Local-Global Cross Attention (PLGCroA) Block, ...
In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), many approaches based on the CNN-RNN frame...
上图示意的是 Focal Attention 的流程图,其实很好理解,在上图描述了三层结构。Sw是每层特征图池化核的大小,代表了粗细粒度的不同,在图中三层分别为 1/2/4;Sr代表了池化后的每个窗口大小;Sp代表了每个局部注意力窗口的大小,图中为 4。 在感受野一样的情况下,参数得到了大幅减少: ...