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...
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...
Our proposed Dual-CNN decoder consists of a sentence CNN with region attention and a word CNN. The sentence CNN captures the relationships among sentences within a paragraph to guarantee the coherence at the sentence level. The word CNN is responsible for generating words in the paragraph. With ...
The framework uses a "dual-CNN" to simultaneously process inputs of two forms, i.e., temporal or time-frequency. The inputs of each branch may either consist of organized epochs of multiple modalities or their time-frequency representations. The fused deep features of the original multi-modal...
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. ...
A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between LabelsCNNMulti-labelClassificationLabel co-occurrence dependenciesIn recent years, deep convolutional neural network (CNN) has demonstrated its great power in image classification. In real world, there are ...
Designing and Training of A Dual CNN for Image Denoising This paper is conducted by Chunwei Tian, Yong Xu, Wangmeng Zuo, Bo Du, Chia-wen Lin and David Zhang. It is implemented by Pytorch. And it is reported by Cver at https://wx.zsxq.com/mweb/views/topicdetail/topicdetail.html?topic...
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...
Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we...
Dual Learning for Machine Translation:微软的刘铁岩团队2016年在NIPS中提出的一种新的深度学习机器翻译框架。 NMT进行训练时往往需要上千万带标注的数据,但实际上常常面临标注数据不足的情况,特别是对于小语种的翻译,为了解决NMT训练样本不足问题,本文提出了一种对偶学习的模式(dual-learning),让模型自动的从未标记的数...