总结一句话:绝对位置编码不灵活,并且没有平移不变性;相对位置编码有平移不变性,但增大计算开销,需要改变Transformer API,并且无法提供图片分类所需的绝对位置信息;而CPE克服二者缺点,很厉害就是了。 Model 1. Conditional Positional Encoding 作者认为,一个成功的位置编码应该具有如下优良品质: 1.对输入序列排列顺序敏感...
Conditional Positional Encodings for Vision Transformers https://github.com/Meituan-AutoML/Twins 2. Summary 本文主要是对Transformer中的Positional Encoding问题进行了探索,之前的PE都存在一定的问题:例如无法适应不同长度的序列、不具有平移不变性等。 基于这些问题,本文提出了Conditional Positional Encoding。主要方法...
Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings. Benefit from the conditional positional encoding scheme, we obtain state-of-the-art results on the Image...
We also hope this work will inspire further theoretical study of positional encoding in vision MLPs and could have a mature application as in vision Transformers.Our code is based on the pytorch-image-models, attention-cnn, swim-transformer,vision-Permutator...
TRANSFORMER modelsENCODINGPOINT processesPOINT cloudCOMPUTER visionVIDEO codingThe fast development of novel approaches derived from the Transformers architecture has led to outstanding performance in different scenarios, from Natural Language Processing to Computer Vision. Recently, they achieved im...
encoding as well. Along the way I give a proposal for implementing abi-directional relative positional encoding, based on the architecture of Transformer-XL. I haven’t been able to find anyone that discusses this, so please chime in if you can shed some light on whether anyone has pursued ...
Positional Encoding 是数据输入Transformer的模型之前的必要处理,其目的是让模型得知序列的相对位置信息。在这里记录一下采用正弦余弦的Positional Encoding的实现方法,以及其高效实现方法。 首先给出公式: P)=sin(pos100002idmodel)PE(pos,2i+1)=cos(pos100002idmodel) ...
这里要区别一下隐式神经表示与 Transformer 中位置编码的不同作用与含义 2.4 设计PIP 有了2.1-2.3 的铺垫,就可以开始在 DIP 的基础上进行改进设计 PIP 了 1.首先作者提出,图像 low-level 任务以及video、3D任务可以从隐式神经表示的角度处理,即让MLP学习坐标到信号的映射。因此,将卷积替换为MLP,conv(CNN) ->...
In addition, MomnetNet effectively applies positional encoding techniques, which are commonly applied in transformer architectures, to the multi-stage temporal convolution network. By using the positional encoding techniques, MomentNet can provide important temporal context, resulting in higher phase ...
Liu et al. [24] used the transformer encoder layers to learn dependencies between different positions in multi-scale features, and combined LSTM to aggregate features from different encoding layers, ultimately using the transformer decoder layer to generate descriptive sentences. Zhuang et al. [29] ...