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SRFlow: Learning the Super-Resolution Space with Normalizing Flow
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VConference Paper -
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Seven ways to improve example-based single image super resolution
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)Conference Paper -
Some like it hot - visual guidance for preference prediction
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)Conference Paper -
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GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
(2020)Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these applications share fundamental challenges, such as large displacements, pixel-accuracy, and appearance changes, they are currently addressed with specialized network architectures, designed for only one particular task. This severely limits the generalization ...Conference Paper -
Learning Accurate Dense Correspondences and When to Trust Them
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference Paper -
Deep Burst Super-Resolution
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference Paper -
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)In this paper, we propose a Hierarchical Learned Video Compression (HLVC) method with three hierarchical quality layers and a recurrent enhancement network. The frames in the first layer are compressed by an image compression method with the highest quality. Using these frames as references, we propose the Bi-Directional Deep Compression (BDDC) network to compress the second layer with relatively high quality. Then, the third layer frames ...Conference Paper -
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
(2021)Advances in Neural Information Processing Systems 33The feature correlation layer serves as a key neural network module in numerous computer vision problems that involve dense correspondences between image pairs. It predicts a correspondence volume by evaluating dense scalar products between feature vectors extracted from pairs of locations in two images. However, this point-to-point feature comparison is insufficient when disambiguating multiple similar regions in an image, severely ...Conference Paper