Attention mechanismEncoder-decoder frameworkTransformerVideo deblurring holds significant importance for the field of motion photography such as hand-held photography, UAV aerial photography, and vehicle video. However, the non-uniform dynamic blur brings great challenges to the existing methods to realize ...
首先回顾一下 attention 操作, 位置 m 的 query 和位置 n 的 key 会做一个内积操作:fq(xm,m)=[q(1)mcos(mθ)−q(2)msin(mθ),q(2)mcos(mθ)+q(1)msin(mθ)]fk(xn,n)=[k(1)ncos(nθ)−k(2)nsin(nθ),k(2)ncos(nθ)+k(1)nsin(nθ)]<fq(xm,m),fk(xn,n)>=(q(1)mco...
NoPE是指忽略位置嵌入，并依靠Decode-only架构中的单向注意力或者说Causal Attention来隐式学习位置信息，...
后来也被一些工作用到 (比方说 ModuleFormer: Modularity Emerges from Mixture-of-Experts) 重用attention矩阵 苏神在回答里面的(1)和(2)点提到这种方式需要算两遍attention矩阵,非常耗时。但是实际上CoPE原文推崇的是复用attention logit QK^T 来同时算softmax和 (sigmoid cumsum based soft)相对位置,在kernel里面...
At the heart of data analysis lies the role of adata analyst, who systematically gathers, processes, and conducts statistical analyses on datasets. Their duties encompass several key responsibilities. Firstly, data cleaning and preparation involve filtering data, handling missing values, and ensur...
Position information in Computer Science refers to the method of encoding the position of tokens in a sequence, such as in self-attention mechanisms, to capture the order information. It can be achieved through techniques like position encoding or learned position embedding to enhance the performance...
feat: unify configuration#1558: Connects with the unification of configuration handling inconfig.rs. feat: unify configuration reading#1560: Related to error handling changes inconfig.rs. refactor: rename --tracing to --inspect#1576: Reflects a broader effort to improve code clarity, similar to ch...
Unsupervised Discovery of Object Landmarks as Structural Representations #Object Detection CVPR 2018 Oral, 今年 CVPR Landmark 和 Attention 这两个词出现的频率很高。 Landmark Detector 本文采用的是名为 hourglass 的网络构架,以图片作为输出,该网络输出 k+1 个 channel,含有 k 个 landmark 和背景。对不...
补充一个sum()实例,便于下面Attention层sum()操作的理解: 1classPositionAwareAttention(nn.Module):2"""3A position-augmented attention layer where the attention weight is4a = T' . tanh(Ux + Vq + Wf)5where x is the input, q is the query, and f is additional position features.6"""78def_...
This is a more standard version of the position embedding, very similar to the one used by theAttention is all you needpaper, generalized to work on images. 这是一个更标准版本的位置嵌入,与 Attention is all you need使用的非常相似,通用用于处理图像。