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When combined with the standard pixel-wise loss, both our new loss term and our iterative refinement boost the quality of the predicted delineations, in some cases almost doubling the accuracy as compared to the same classifier trained with the binary cross-entropy alone. We show that our ...
论文阅读-Hierarchical Cross-Modal Talking Face Generation with Dynamic Pixel-Wise Loss 论文链接: http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Hierarchical_Cross-Modal_Talking_Face_Generation_With_Dynamic_Pixel-Wise_Loss_CVPR_2019_paper.html 概述 关键词: 高级空间, 像素抖动, GAN模型 ...
蒸馏的loss如下所示: 其中\ell 表示contrastive loss,下文会有更详细的形式。 s_i 和t_i 分别表示student和teacher的feature maps的第i个像素(文章假设了student和teacher的输出feature maps具有相同的spatial size,即 H\times W ,由于目前流行的网络结构大部分都是总步长为32,所以这一点要求并不苛刻), \{n^k...
The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art ...
between predictions and ground-true objects: (1) cross-entropy loss between the predicted and ground-true classes; (2) focal loss [80] between the predicted center heatmaps and ground-true center heatmaps; (3) size loss computed by the L1 loss between predicted and ground true size....
此loss的优势: 1. 没有超参数; 2. Inlier pixel数量 >> OoD pixel数量, 这种loss由于其他的loss比如 hinge loss,对OoD pixel有更好的优化和收敛效果. Context-robust Contrastive Learning(CoroCL) 大部分方法难以区分, 不熟悉的inlier像素 vs 潜在的OoD像素 ...
设计了一种自动编码器结构来估计三维坐标和每个像素的期望误差...:1、提出了一种新的6D姿态估计框架Pix2Pose,该框架在训练过程中使用无纹理的3D模型从RGB图像中稳健地回归出目标的像素级3D坐标。2、一种新的损耗函数:transformerloss,用于 物体6D姿态估计
先说一下,做data augmentation的目的是为了减少噪声对模型的影响,希望模型真正学习到目标的特征,由于yolov3的该模块特别典型,故以此说明,就是包括以下部分: 在这之前先进行了图像融合,就是随机对图像融合,: 我们只看图像操作部分先确定融合后的图像为两个图像最大
{d}\)which takes a SEM image as input and outputs a depth map. Furthermore, depending on the loss function, we use a discriminator network with a mapping functionD. This network takes a SEM image and a corresponding predicted depth map as input and outputs an error-parameter score that ...