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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 -
Warp Consistency for Unsupervised Learning of Dense Correspondences
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearance changes, which are ubiquitous in geometric and semantic matching tasks. Moreover, methods relying on synthetic training pairs often suffer from poor generalisation to real data.We propose Warp Consistency, an unsupervised ...Conference Paper -
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image ...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 -
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We propose Probabilistic Warp Consistency, a weakly-supervised learning objective for semantic matching. Our approach directly supervises the dense matching scores predicted by the network, encoded as a conditional probability distribution. We first construct an image triplet by applying a known warp to one of the images in a pair depicting different instances of the same object class. Our probabilistic learning objectives are then derived ...Conference Paper -
Deep Burst Super-Resolution
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)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 -
Video Object Segmentation with Episodic Graph Memory Networks
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IIConference Paper -
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