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Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth estimation, most existing state-of-the-art learning techniques predict a single scalar ...Conference Paper -
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Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
(2023)The Eleventh International Conference on Learning RepresentationsConference Paper -
Generative Flows with Invertible Attentions
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference 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 -
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 -
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Structured Output SVM Prediction of Apparent Age, Gender and Smile From Deep Features
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognision Workshop (CVPRW 2016)Conference Paper -
Deep Unfolding Network for Image Super-Resolution
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels and noise levels under a unified MAP (maximum a posteriori) framework, learning-based methods generally lack such ...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