In this paper, we focus on scaling and we aim to equip the Siamese network with additional built-in scale equivariance to capture the natural variations of the target a priori. We develop the theory for scale-equivariant Siamese trackers, and provide a simple recipe for how to make a wide ...
The effectiveness of Convolutional Neural Networks (CNNs) has been substantially attributed to their built-in property of translation equivariance. However, CNNs do not have embedded mechanisms to handle other types of transformations. In this work, we pay attention to scale changes, which regularly...
在非对称Linex损失下,对具体的经济问题,给出了最优位置同变和最优位置尺度同变预测量。2) Location and scale equivariance 位置尺度同变性3) Huber place-scale estimate 尤伯位置尺度同时估计4) location equivariant 位置同变 1. Under the asymmetric loss,the best location equivariant and best location-...
在中等规模或小规模数据集上(比如ImageNet),基于Transformer的模型效果还是比不过基于CNN的模型。原因是Transformer模型相较于CNN来说,缺乏一些归纳偏置(Inductive Bias)。这里归纳偏置是指CNN的平移不变性( translation equivariance)和局部感知性(locality)。 在大规模数据集上(14M-300M规模图片),基于Transformer的模型效...
在中等数据集训练下,ViT比同规模ResNet精度低,这是由于缺少对图像数据的归纳偏置(极少:MLP用到了12;切patch和微调用到了3),而CNN有了这些先验信息,即需要更少的数据来训练。但实验发现大规模预训练胜过归纳偏置。 inductive biases归纳偏置 “translation equivariance”平移等变性:卷积结果不依赖于物体在图像中的位...
clustering pixels transformed under two photometric alterations should ideally be closer to their respective cluster centers and also closer to the cluster centers of the other photometric transformation. Geometric equivariance implies that when an image undergoes geometric transformations like scaling, the re...
Transformer 缺乏 CNN 固有的一些归纳偏置(inductive biases) [TODO],如平移等变性(translation equivariance)和局部性(locality),因此在数据量不足的情况下训练时不能很好地泛化。 在较大的数据集(14 M-300 M 图像)上 大规模训练已经胜过归纳偏置 ViT 在进行足够规模的预训练再转移到数据点较少的任务时效果拔群...
Chapter 14 in the book (Lindeberg 1993b) and the paper (Lindeberg and Garding 1997) describe the notion of affine Gaussian scale space, with its closedness property under affine image transformations, referred to as affine covariance or affine equivariance. ...
而考虑分类任务重的 CNNs ,无论平移位移只要仍使得物体在图像边界内,分类结果都是一样的。这就是CNNs的平移不变性。神经网络的平移不变性与等变性 - 1. What is Translational Invariance and Equivariance? - 知乎 (zhihu.com)】 二维邻域结构的使用非常谨慎:在模型开始时,通过将图像切割成块,并在微调时调整...
CNN中的inductive bias包含locality,two-dimensional neighborhood structure和translation equivariance。而在ViT中只有MLP具有locality和translation equivariance,attention是global的;且two-dimensional neighborhood structure仅存在于将图片分割为patch;而position embedding和spatial information都需要从头学起。在微调时,若图片的...