multi-attention multi-class constraint(MAMC),加强注意力之间的联系。 网络架构 两个注意力分支的网络架构如上图所示。 OSME(one-squeeze multi-excitation) 是一种在弱监督下的,基于注意力机制的部件定位的方法,之前的工作包括两种情况: 部件检测,往往将部件检测和特征提取分开,这样的话会增大计算开销 来源于相应...
Ming Sun, Yuchen Yuan, Feng Zhou, and Errui Ding. Multi-attention multi-class constraint for fine-grained image recognition. arXiv preprint arXiv:1806.05372, 2018.M. Sun, Y. Yuan, F. Zhou, and E. Ding. Multi-attention multi-class constraint for fine-grained image recognition. In ECCV,...
Multi-Attention Multi-Class Constraint for Fine-grained Image 是百度发表在ECCV2018上的工作,论文的主要贡献是针对输入的图像对,能够提取每张图像的多个区域,然后按照类别和区域的不同,采用Triplet Loss和Softmax Loss来训练网络。论文下载地址: https://arxiv.org/pdf/1806.05372.pdfarxiv.org/pdf/1806.05372...
总体来说，“Multi-Attention Multi-Class Constraint for Fine-grained Image”通过创新的OSME模块和MAMC约束，提供了一种高效、实用的细粒度图像识别解决方案，为该领域的发展带来了新的突破。
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition(ECCV2018) Multi-AttentionMulti-Class ConstraintforFine-grainedImageRecognition(ECCV2018) 之前的笔记: https...是:一站式多激励模块(OSME)生成多个Attention并通过MAMC 使得同注意力门同类别:sasc,就是其正样本同一个注意力门的特征向量 ...
1, crop pest detection has some challenging issues that affect the accuracy of insect pest detection methods, such as multi-class, multi-scale, tiny size of pest objects, unbalanced data for multi-class, and sparse pest distribution. To improve the detection accuracy of field insect pests, an...
We set an adjacency matrix to filter irrelevant information and constraint update direction as shown in Eq. (6). (6)Ak=RNN(A,Ek) where Ak is the adjacency matrix at the k-th layer. A is the label adjacency matrix, the element ai , j is equal to one when v i and v j have the...
It can capture subtle inter-class differences in FGVC. For example, [30] proposed a method that consisted of two parts: a differentiable one-squeeze multi-excitation (OSME) model and a multi-attention multi-class (MAMC) constraint. The OSME captured features from multiple attention regions to...
而论文提出 multi-attention multi-class constraint (MAMC) 来探索更丰富的object-parts的相关性。 假设现有训练图像{(x,y),...}共K张, 和N-pair Loss论文的做法一样,采样出N对样本:B={(x_i,x^+i,y_i),...},其中x_i和x^+_i都属于y_i类,集合B中共有N类图片。 对于每对样本(x_i,x^+_i...
The constraint function g(·)g· is parameterized by 𝜃θ, where 𝜃=0θ=0 indicates the deletion of the convolutional kernel, while 𝜃=1θ=1 preserves it. The performance constraint is designed as: g(𝜃)=∥Δℒ1(𝜃),Δℒ2(𝜃),…,Δℒ𝑇(𝜃)∥∞gθ=ΔL1θ,ΔL...