• Publications
  • Influence
Learning to See in the Dark
TLDR
A pipeline for processing low-light images is developed, based on end-to-end training of a fully-convolutional network that operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data.
Photographic Image Synthesis with Cascaded Refinement Networks
  • Qifeng Chen, V. Koltun
  • Computer Science
    IEEE International Conference on Computer Vision…
  • 28 July 2017
TLDR
It is shown that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective.
Single Image Reflection Separation with Perceptual Losses
TLDR
The approach uses a fully convolutional network trained end-to-end with losses that exploit low-level and high-level image information and proposes a novel exclusion loss that enforces pixel-level layer separation.
KNN Matting
TLDR
The matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by using K nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm giving competitive results with sparse user markups.
Fast Image Processing with Fully-Convolutional Networks
TLDR
This work presents an approach to accelerating a wide variety of image processing operators using a fully-convolutional network that is trained on input-output pairs that demonstrate the operator’s action, and demonstrates that the presented approach is significantly more accurate than prior approximation schemes.
A Simple Model for Intrinsic Image Decomposition with Depth Cues
  • Qifeng Chen, V. Koltun
  • Computer Science
    IEEE International Conference on Computer Vision
  • 1 December 2013
TLDR
The model analyzes a single RGB-D image and estimates albedo and shading fields that explain the input and decomposes the shading field to build in assumptions about image formation that help distinguish reflectance variation from shading.
Semi-Parametric Image Synthesis
TLDR
A semi-parametric approach to photographic image synthesis from semantic layouts that combines the complementary strengths of parametric and nonparametric techniques is presented.
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
TLDR
Experimental results demonstrate that the presented approach substantially outperforms recent deep learning work, and performs on par with highly optimized state-of-the-art heuristic solvers for some NP-hard problems.
Interactive Image Segmentation with Latent Diversity
TLDR
The proposed architecture couples two convolutional networks and is trained to synthesize a diverse set of plausible segmentations that conform to the user's input, which retains compatibility with existing interactive segmentation interfaces.
Speech Denoising with Deep Feature Losses
TLDR
An end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly, which outperforms the state-of-the-art in objective speech quality metrics and in large-scale perceptual experiments with human listeners.
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