文章地址:GLASS: Global to Local Attention for Scene-Text Spotting: GLASS: Global to Local Attention for Scene-Text Spottingarxiv.org/abs/2208.03364 Abstract 本篇文章针对端到端的Scene-Text Spotting 任务,提出里一个新颖的Module :GLASS(Global-to-Local Attention mechaniSm for text Spotting)。这个...
Abstract 本篇文章针对端到端的 Scene-Text Spotting 任务,提出里一个新颖的 Module :GLASS (Global-to-Local Attention mechaniSm for text Spotting)。这个模块结合了 image 中的 global feature(大尺度,低分辨率)和 local feature(小尺度,高分辨率)对任务进行端到端的训练。同时文章还提出了一个新的基于旋转的损...
To enhance the model's perception ability towards the critical part of the fault signal, the local attention mechanism is embedded into the proposed method. Finally, the proposed method is validated by applying it to experimentally acquired vibration signal data of reciprocating pumps. Excell...
In this paper, a 3D dental model segmentation network based on local attention mechanism is proposed to improve segmentation performance on teeth boundaries. Firstly, multi-scale local spaces are constructed for 3D mesh data of raw dental model. Secondly, attention weights are learned based on the...
4) 上下文非局部注意力机制 (Contextual Non-Local Attention Mechanism): 通过线性投影,将图像特征集合映射为query和value,即: , ,将文本特征集合映射为key和value,即: , 。 ① 计算第a个图像value对应的文本特征: 首先计算第a个query和第b个key的余弦相似度,即: ...
非局部注意力机制(nonlocal attention mechanism)是一种用于计算机视觉任务的注意力机制,其目的是在一个图像或视频中建立全局的关联。 注意力机制旨在模拟人类视觉系统的特征提取过程。在图像识别任务中,对于感兴趣的目标,并不是所有的细节都是重要的。通过引入注意力机制,模型可以学习到对于不同部分的关注程度,从而引导...
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。
However, the sequence‐to﹕equence model has a time complexity of O(n2) for an input length n when using the attention mechanism technique for high performance. In this study, we propose a linear‐time Korean morphological analysis model using a local monotonic attention mechanism relying on ...
1、 such methods suffer from attention-drift problem because high similarity among encoded features leads to attention confusion under the RNN-based local attention mechanism. 2、RNN-based methods have low efficiency due to poor parallelization. ...
As you can see, the model is able to generate some really good-looking images, but not all generated images are photo-realistic. We expect that training bigger architectures, such as BigGAN, with our 2-d local sparse attention layers, will improve significantly the quality of the generated im...