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Actions Projects Security Insights Additional navigation options main 1Branch0Tags Code README MIT license Flowformer (ICML 2022) Flowformer: Linearizing Transformers with Conservation Flows 🚩News(2024.07)Mobile-Attention, a mobile-device-tailored version of Flowformer, has been published in ICML 2024...
https:// 源代码:https://github.com/drinkingcode 一、算法框架 FlowFormer光流算法框架 二、源码解读 2.1 FlowFormer源代码解读 根据算法框架,我们先看一下FlowFormer类的代码,具体如下 #FlowFormer类,对应Fig.1 算法框架 class FlowFormer(nn.Module): def __init__(self, cfg): super(FlowFormer, self)....
论文地址:https://arxiv.org/abs/2203.16194 论文源代码:https://github.com/drinkingcoder/FlowFormer-Official 一、算法框架 FlowFormer光流算法框架 二、源码解读 通过MemoryDecoder实现从Cost Memory到Flow的计算: class MemoryDecoder(nn.Module): def __init__(self, cfg): super(MemoryDecoder, self).__ini...
代码:github.com/thuml/Flowfo 1. 引言 追求任务通用模型是基础模型研究领域的核心目标之一,也是深度学习研究通向高级智能的必经之路。 近年来,得益于注意力机制(Attention Mechanism)的通用关系建模能力,Transformer在众多领域已经展现出了优秀的效果,并且逐步呈现出通用模型的态势。但是,其核心组件注意力机制随着输入序列...
This branch is 3 commits ahead of, 3 commits behind drinkingcoder/FlowFormer-Official:main.Folders and filesLatest commit rinze70 batch_size to 2 00b3288· Sep 28, 2022 History17 Commits alt_cuda_corr model&code Jul 26, 2022 assets first commit Jul 20, 2022 ...
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论文源代码:https://github.com/drinkingcoder/FlowFormer-Official 一、算法框架 FlowFormer光流算法框架 二、源码解读 2.1 CostPerceiverEncoder 在【20230401】【光流算法】【FlowFormer光流算法源码解读02】 - 知乎 (zhihu.com)中,已经对生成Cost Volume 4D 特征张量进行了详细说明,Cost Volume如何形成Cost Memory,则...
论文源代码:github.com/drinkingcode 一、算法框架 FlowFormer光流算法框架 二、源码解读 生成4D Cost Volume 源码解读 在上文胡说数字化:【20230401】【光流算法】【FlowFormer光流算法源码解读01】中我们提到,利用 cost_volume = self.corr(feat_s, feat_t) 得到H∗W∗H∗W 4D Cost Volume,cost_volume ...
代码:GitHub - sail-sg/iFormer: iFormer: Inception Transformer 受网络流理论启发,本文提出任务通用的骨干网络Flowformer,将竞争机制引入到注意力计算中,实现线性复杂度,没有引入新的归纳偏好,在长序列、视觉、自然语言、时间序列、强化学习五大任务上取得优秀效果。