提出memory-augmented attention模块,通过引入general video prior,辅助视频超分辨; 提出Parkour 数据集,该数据集中视频帧间存在大的位移,本文为视频超分辨应用于拥有大位移的视频提供了一个benchmark; 3、方法 网络架构 MANA由五部分组成,首先encoder(5个残差块)提取相邻帧以及当前帧的特征;然后经过Cross-Frame Non-Loc...
To address the challenge of detecting subtle anomalies and those with motion characteristics, we propose the integration of self-attention in the discriminator model. Our proposed model MAAD-GAN enhances the ability to distinguish between real and generated samples, ensuring that anomalous samples are ...
这时候我们按照最开始所说的方法,计算Query和所有key的关联矩阵,但是可以想象,这样计算的话,关联矩阵会非常大。因此,为了减少关联矩阵的计算开销,本文提出一种两阶段的attention机制,来减少计算开销。主要分为两步:(1)首先对memory中的之前帧逐帧计算ROI,这个ROI的意义就是缩小关联矩阵的计算范围,原来的关联矩阵计算是...
Neural Machine Translation with Key-Value Memory-Augmented AttentionFandong Meng, Zhaopeng Tu, Yong Cheng, Haiyang Wu, Junjie Zhai, Yuekui Yang, Di WangTencent AI Lab{fandongmeng,zptu,yongcheng,gavinwu,jasonzhai,yuekuiyang,diwang}@tencent.comAbstractAlthough attention-based Neural Machine Transla...
Attention models used for problems such as image captioning typically depend on the image under consideration, as well as the previous sequence of words that come before the word currently being generated. While these types of models have produced impressive results, they are not able to model ...
, sometimes indepently of the image.选择性的关注图像中的某些区域是很重要的策略。我们从最近的memory-augmentedneuralnetworks以及 co-attention mechanism 得到启发,本文中,我们利用memory-networks来记忆rare events,然后用memory-augmented 论文笔记 Visual Question Answering with Memory-Augmented Networks(CVPR2018) ...
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs due to the quadratic time and space complexity of attention mechanisms and the growing memory consumption of the key-value...
几篇论文实现代码:《Memory-Augmented Non-Local Attention for Video Super-Resolution》(CVPR 2022) GitHub: github.com/jiy173/MANA [fig7] 《Open-world Semantic Segmentation for LIDAR Point Clouds》(E...
3.3.2、Attention来进行内存选址 在MemAE中,记忆M被设计成显式地记录训练过程中的原型正常模式。我们将内存定义为内容可寻址内存[38,29],采用寻址方案,根据内存项和查询z的相似性计算注意权值w。如图1所示,我们通过softmax操作计算每个权值 : d (·;·)表示相似性度量。类似于[32],我们定义了d(·;·)as余弦...
3.3.2、Attention来进行内存选址 3.3.3、稀疏寻址的硬收缩 3.4、训练 4、实验 4.1、在图像数据上的实验 4.1.1、想象记忆是如何运作的 4.2、在视频异常检测上的实验 4.3、网络安全数据实验 4.4、消融研究 4.4.1、包含稀疏组件的研究 4.4.2、和稀疏正则化的比较 5、结论 广告 相关产品与服务 联邦学习 联邦...