4 citations · 10 across the 9 of their papers we have counts for
11 papers
RGraph-Eval: Task-Grounded Scene Graphs for Evaluating Robot Video Generation
Aleksandr Lazarev, Sofia Zimina, Roman Kuznetsov +3
Generated robot videos can look convincing while depicting the wrong interaction: a different object is moved, a grasp disappears, or a placement ends outside its destination. We i…
Data Unlearning via Inverse Distillation
Aleksei Leonov, Nikita Kornilov, Zhenhe Zhang +3
Multi-step matching models, including flow and diffusion models, produce high-quality outputs but incur substantial inference costs and may reproduce unwanted components of their t…
Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport
Xavier Aramayo Carrasco, Grigoriy Ksenofontov, Aleksei Leonov +2
The Entropic Optimal Transport (EOT) problem and its dynamic counterpart, the Schrödinger bridge (SB) problem, play an important role in modern machine learning, linking generative…
Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs)
Nikita Kornilov, David Li, Tikhon Mavrin +5
While achieving exceptional generative quality, modern diffusion, flow, and other matching models suffer from slow inference, as they require many steps of iterative generation. Re…
One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation
Daniil Selikhanovych, David Li, Aleksei Leonov +6
Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of several methods to accelerate d…
A Modular Conditional Diffusion Framework for Image Reconstruction
Magauiya Zhussip, Iaroslav Koshelev, Stamatis Lefkimmiatis
Diffusion Probabilistic Models (DPMs) have been recently utilized to deal with various blind image restoration (IR) tasks, where they have demonstrated outstanding performance in t…
Robust Two-View Geometry Estimation with Implicit Differentiation
Vladislav Pyatov, Iaroslav Koshelev, Stamatis Lefkimmiatis
We present a novel two-view geometry estimation framework which is based on a differentiable robust loss function fitting. We propose to treat the robust fundamental matrix estimat…
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems
Iaroslav Koshelev, Stamatios Lefkimmiatis
In this work we present a novel optimization strategy for image reconstruction tasks under analysis-based image regularization, which promotes sparse and/or low-rank solutions in s…
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization
Stamatios Lefkimmiatis, Iaroslav Koshelev
We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the -vector…
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution
Iaroslav Koshelev, Daniil Selikhanovych, Stamatios Lefkimmiatis
In this work, we study the problem of non-blind image deconvolution and propose a novel recurrent network architecture that leads to very competitive restoration results of high im…
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
Ren Yang, Radu Timofte, Jing Liu +69
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Di…