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Learning Accurate Dense Correspondences and When to Trust Them

CVPR 2021 Oral A method that gives you accurate dense optical flow and correspondences with robust uncertainty.

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

CVPR 2021 Oral A novel unpaired learning formulation for conditional normalizing flows with applications to learning image degradations.

Deep Burst Super-Resolution

CVPR 2021 An attention based architecture and real-world dataset for burst super-resolution.

The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture

CVPR 2021 We tackle the problem of convolutional neural network design by adjusting the channel configurations of predefined networks.

Few-Shot Classification By Few-Iteration Meta-Learning

ICRA 2021 An optimization-based meta-learning approach for few-shot classification.

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

NeurIPS 2020 Dataset and method for generating vector graphics.

GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network

NeurIPS 2020 A fully differentiable dense matching module for your correspondence or optical flow network.

How to Train Your Energy-Based Model for Regression

BMVC 2020 Investigating how to train a deep energy-based model for accurate regression.

Learning What to Learn for Video Object Segmentation

ECCV 2020 Oral An optimization-based few-shot learner for VOS.

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

ECCV 2020 Spotlight Normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input.