We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are first combined with a set of learned position biases, the result of which is multiplied with the query in an el...
NLP-Attention-Free-Transformer This repository contains the implementation of An Attention Free Transformer in PyTorch. It is trained on the movie dialog dataset using the architecture mentioned in the paper. Training the model The data is first processed into sub-word tokens prior to training by ...
Transformer ( Attention Is All You Need)仅仅依赖于注意力机制,而完全(entirely)没用使用循环或者是卷积。使用了两个机器翻译的实验,显示此模型效果特别好。(一开始做的是机器翻译_相对来说比较小的领域,但是随着之后bert、gbt把这个架构运用在更多的自然语言处理的、任务上,工作才出圈了。以至于之后运用在cv上 )...
The attention function used by the transformer takes three inputs: Q,K,V. The equation used to calculate the attention weights, which is scaled by a factor of square root of the depth is:Multi-head attentionMulti-head attention consists of four parts: Linear layers、Multi-head attention、...
第1章 TextMonkey : An OCR-Free Large Multimodal Model for Understanding Document 摘要 1 INTRODUCTION 2 RELATED WORKS 2.1 OCR-Model-Driven Methods 2.2 OCR-Free Methods 3 METHODOLOGY 3.1 Shifted Window Attention 3.2 Token Resampler 3.3 Position-Related Task 3.4 Dataset Construction 3....
PatchFormer: an efficient point transformer with patch attention Zhang等人(2022) PatchFormer:具有补丁注意力的高效点转换器 引入了补丁注意力(PAT)来自适应地学习一组小得多的基础来计算注意力图。通过基于这些基础的加权求和,PAT 不仅捕获全局形状上下文,而且还实现了输入大小的线性复杂度。此外,我们提出了一个轻...
ASTNAT: an attention-based spatial–temporal non-autoregressive transformer network for vehicle trajectory prediction. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10548-w Download citation Received04 January 2024 Accepted03 October 2024 Published26 November 2024 DOIhttps://doi...
几篇论文实现代码:《LiDAR R-CNN: An Efficient and Universal 3D Object Detector》(CVPR 2021) GitHub:https:// github.com/TuSimple/LiDAR_RCNN [fig1] 《PCLs: Geometry-aware Neural Reconstruction of 3D...
The attention function used by the transformer takes three inputs: Q,K,V. The equation used to calculate the attention weights, which is scaled by a factor of square root of the depth is:Multi-head attentionMulti-head attention consists of four parts: Linear layers、Multi-head attention、...
Ultimate-Awesome-Transformer-Attention This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites. This list is maintained by Min-Hung Chen. (Actively keep updating) If you find some ignored papers, feel free to create pull re...