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This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that ima... Yang, J.Wright, J.Huang, T.Ma, Y. - 《IEEE Transactions on Image Processing》 被引量: 3921发表: 2010年 U-Net: Convolutional Networks...
As we all know, the update of the parameters of the previous convolutional layer will cause the data distribution of the later input layer to change, resulting in a large difference in the data distribution of the convolutional feature layer. Therefore, there will be large differences between fea...
To alleviate the cost of collecting and annotating large-scale point cloud data, Zhang and Zhu [31] propose an unsupervised learning approach to learn features from an unlabeled point cloud dataset by using part contrasting and object clustering with deep graph convolutional neural networks (GCNNs)...
Semantic segmentation on a high-dimensional sparse tensor: 4D Spatio Temporal ConvNets, CVPR'19 The first fully convolutional metric learning for correspondences: Universal Correspondence Network, NIPS'16 3D Registration Network with 6-dimensional ConvNets: Deep Global Registration, CVPR'20 Projects using...
Zhong J, Li N, Kong W, Liu S, Li TH, Li G (2019) Graph convolutional label noise cleaner: train a plug-and-play action classifier for anomaly detection. In: IEEE conference on computer vision and pattern recognition, CVPR 2019, Long Beach, CA, USA, June 16–20, 2019, Computer Visio...
Deep convolutional neural networks for accurate somatic mutation detection. Nat. Commun. 10, 1–10 (2019). Article CAS Google Scholar Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and ...
Keywords: Super-Resolution, Deep Convolutional Neural Network 1 Introduction Super-resolution (SR) is a computer vision task that reconstructs a high-resolution (HR) image from a low-resolution (LR) image. Specifically, we are concerned with single image super-resolution (SISR), which performs ...
3D seman- tic segmentation with submanifold sparse convolutional net- works. In IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), pages 9224–9232, 2018. [15] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conf. Comput. Vis. Pattern...
Noise2Self-CNN: Noise2Self denoising via Convolutional Neural Networks (CNN). This is the original approach of Noise2Self. In our experience this is typically slower to train, and more prone to hallucination and residual noise than FGR. ...