为了解决这个问题,我们提出了一种双卷积神经网络(DualCNN),它可以联合估计结构和细节。 DualCNN由两个分支组成,一个是用于估计结构的浅子网,另一个是用于估计细节的深度子网。 DualCNN的模块化设计使其成为各种低级视觉问题的灵活框架。 当端到端训练时,DualCNN对于针对每项任务专门设计的最先进方法表现出色。 Propos...
4 Dalian University of Technology 5 SenseTime Research 6 Tencent Youtu Lab 7 UC Merced DualCNN。将structure与details分别训练,并在最后整合,loss函数由S,D以及S和D相加重构出的最终结果与实际标签之间的loss,三者组成。具体实现细节如下: 两个net的选择:对于结构的net-s只用了三层,对于细节的net-d用了20层...
摘要 CNN架构通常对内存和计算资源的要求较高,这使得它们在硬件资源有限的嵌入式系统中难以实现。我们提出了一种用于构建轻量级深度神经网络的双卷积核(DualConv)方法。DualConv结合了$3 \times 3$和$1 \times 1$的卷积核,同时处理相同的输入特征图通道,并利用组卷积技术高效地排列卷积滤波器。DualConv可以应用于任...
Dual CNNContrastive learningThe cross-teaching based on Convolutional Neural Network (CNN) and Transformer has been successful in semi-supervised learning; however, the information interaction between local and global relations ignores the semantic features of the medium scale, and...
低层视觉问题通常涉及到目标结果的结构和细节部分的估计, 这个Dual Conv包含了两个分支, 分别可以端到端的估计目标结果的结构和细节信息。基于估计的结构的细节信息, 目标结果可分别通过特定问题的成像模型(SR, Edge-Preserve Filter)来得到。 A DualCNN consists of two branches, one shallow sub-...
原文链接:CNN王者归来 | DualBEV:大幅超越BEVFormer、BEVDet4D,开卷! 关注知乎@擎天柱,第一时间获取自动驾驶感知/定位/融合/规控等行业最新内容 针对这些问题,论文提出了一种统一的特征转换方法,适用于2D到3D和3D到2D的视觉转换,通过三种概率测量来评估3D和2D特征之间的对应关系:BEV概率、投影概率和图像概率。这一...
In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. ...
CNN architectures are generally heavy on memory and computational requirements which makes them infeasible for embedded systems with limited hardware resources. We propose dual convolutional kernels (DualConv) for constructing lightweight deep neural networks. DualConv combines 3$imes$3 and 1$imes$1 ...
其二是判别模型D,判断句子是由机器翻译还是人翻译的,其设计有两种方法,即分别采用CNN或者RNN设计,最终发现CNN的效果比LSTM要好,因为LSTM训练时存在negative signal干扰。文章另外一个值得借鉴的是其训练策略,GAN的训练往往非常困难,本文采用了MLE来预训练生成器G,然后再用G生成的样本和真实样本来pretrain D,当D达到...
"Designing and training of a dual CNN for image denoising." Knowledge-Based Systems 226 (2021): 106949. 2. @article{tian2021designing, title={Designing and training of a dual CNN for image denoising}, author={Tian, Chunwei and Xu, Yong and Zuo, Wangmeng and Du, Bo and Lin, Chia-...