In this paper, a partial face recognition problem has been tackled through utilizing patch-wise matching with Convolutional Neural Network (CNN). Firstly, a gallery images are divided into local patches, and each patch is regarded as an independent image. Then, AlexNet architecture is utilized ...
图1 ViT和CNN的对抗鲁棒性对比 使用基于决策的方法攻击ViT存在两方面困难。第一方面源自ViT的结构特征。首先,ViT相对于CNN对低级特征的关注较少,导致ViT的整体噪声敏感性较低。针对ViT的决策攻击需要添加更大量级的随机噪声来寻找初始对抗样...
传统的图像处理主要依赖于卷积神经网络(CNN),CNN 通过局部感受野和层层抽象来提取图像特征。然而,当前许多强大的 MLLMs 的基础架构是 Transformer 模型,这种模型最初为处理文本等序列数据而设计,其核心优势在于通过自注意力机制捕捉序列中元素之间的长距离依赖关系。 将原始图像直接输入 Transformer 面临两大挑战: 巨大的...
【论文笔记】Patch-wise Attack for Fooling Deep Neural Network & Patch-wise++ Perturbation Targeted Attacks,程序员大本营,技术文章内容聚合第一站。
我理解的patchwise training是指对每一个感兴趣的像素，以它为中心取一个patch，然后输入网络，输出则为...
cnn可变patch size cnn可视化特征提取 反卷积 导向反向传播 使用普通的反向传播得到的图像噪声较多,基本看不出模型的学到了什么东西。使用反卷积可以大概看清楚猫和狗的轮廓,但是有大量噪声在物体以外的位置上。导向反向传播基本上没有噪声,特征很明显的集中猫和狗的身体部位上。
SpectralFormer (Hong et al., 2022) is an adaptable backbone network, engineered to accept both pixel-wise and patch-wise inputs. SSFTT (Sun et al., 2022) combines CNN and transformer, incorporating a Gaussian weighted feature tokenizer for sophisticated features exploitation. MorphFormer (Roy et...
SF-MoE包括稀疏块和融合块,如上图所示。稀疏块由多头注意力(MHA)层和混合专家(MoE)层组成。MHA层关注稀疏信号,并在patch(特征子集)之间建立自注意力。然后,MoE层通过向不同的专家分发patch来进一步分离所学的注意力。稀疏块以grid-wi...
Encoder-Decoder采用的是纯MLP结构,类似于Mixer,使用channel-wise、intra-wise、inter-wise三种类型的MLP进行embedding维度、patch内部、patch间的信息提取。 3 多尺寸Patch-Transformer 论文标题:Multi-resolution Time-Series Transformer for Long-term Forecasting ...
The recent advances in Vision Transformer (ViT) have demonstrated its impressive performance in image classification, which makes it a promising alternative to Convolutional Neural Network (CNN). Unlike CNNs, ViT represents an input image as a sequence of image patches. The patch-wise input image ...