In this work, we introduce a multi-level attention mechanism for the task of brain tumor recognition. The proposed multi-level attention network (MANet) includes both spatial and cross-channel attention which not only focuses on prioritizing tumor region, but also maintains cross-channel temporal ...
This paper presents a novel multi-level attention based network for multi-modal depression prediction that fuses features from audio, video and text modalities while learning the intra and inter modality relevance. The multi-level attention reinforces overall learning by selecting the most influential ...
In light of these advancements, this study introduces a multi-scale, multi-level attention network (MSMLA-Net) for deep learning-based LCZ classification. MSMLA-Net integrates a multi-scale (MS) module to generate multi-scale features from the input data and a novel multi-level attention (...
element-wise 表示 Channel Attention 这个在CV 上的 物体检测上用的比较多, 但是在情感分析方面, 大家忽略了channel 维度的Attention,作者在这里用到, 其结构如下图, 比较简单 用Inception V3 得到图片的特征 , 然后过一个channel attention , 其公式是 Spatial Attention 在上一步我们得到 Ac 也就是 经过Channel...
whileneglectingthemodelingofhigh-levelimagesemanticsandtherichspatialcontextofregions.Tosolvethechallenges,weproposeamulti-levelatten-tionnetworkforvisualquestionansweringthatcansimul-taneouslyreducethesemanticgapbysemanticattentionandbenefitfine-grainedspatialinferencebyvisualatten-tion.First,wegeneratesemantic...
第一级attention和第二级attention的产物是对多层memory结构中所有query的结果的attention。 第一级attention,第二级attention和results表示的加权总和就是所求的memory表示:每个result进一步由多个result单元组成。在包含result单元的最后一级memory中,使用的方法是key-value attention。key是槽 k_{a}^{r_{i,j}} 的...
Clinical named entity recognition Convolutional neural network Attention mechanism Residual structure 1. Introduction Named entity recognition (NER) is a fundamental and critical task for other natural language processing (NLP) tasks like relation extraction. With the explosive growth of medical data, clini...
To this end, we propose Multi-level Attention Encoder-Decoder Network (MAED), including a Spatial-Temporal Encoder (STE) and a Kinematic Topology Decoder (KTD) to model multi-level attentions in a unified framework. STE consists of a series of cascaded blocks based on Multi-Head Self-...
To summarize, the results from the Predictive-DQN model were positive, achieving 86.13% accuracy and drawing attention to the effects of Bitcoin-related tweets on Bitcoin futures price changes. Therefore, in this study, to develop an efficient Bitcoin trading strategy, a unique dataset was proposed...
4.4.1. Attention visualization ①选择低级的注意力来可视化,因为这个层次的语义信息对人类更容易理解。 ②分离的分类特征主要集中在眼睛、鼻子和嘴唇上,特别是轮廓。原始人脸注意热图的位置分布类似,但分布范围较小,反应范围较低,因为实际上不存在伪影。尽管假脸是通过不同的方法生成的,但我们的网络关注相似的地方,覆...