• Publications
  • Influence
U-Net: Convolutional Networks for Biomedical Image Segmentation
We present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. Expand
  • 15,275
  • 3233
  • Open Access
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volUMetric images. Expand
  • 1,657
  • 319
  • Open Access
FlowNet: Learning Optical Flow with Convolutional Networks
We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Expand
  • 1,763
  • 304
  • Open Access
Striving for Simplicity: The All Convolutional Net
We propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR100, ImageNet). Expand
  • 2,154
  • 251
  • Open Access
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
  • N. Mayer, Eddy Ilg, +4 authors T. Brox
  • Computer Science, Mathematics
  • IEEE Conference on Computer Vision and Pattern…
  • 7 December 2015
We propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks for scene flow estimation. Expand
  • 916
  • 231
  • Open Access
High Accuracy Optical Flow Estimation Based on a Theory for Warping
We study an energy functional for computing optical flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumptions, and a discontinuity-preserving spatio-temporal smoothness constraint. Expand
  • 2,457
  • 227
  • Open Access
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditionalExpand
  • 1,214
  • 191
  • Open Access
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
  • T. Brox, Jitendra Malik
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 March 2011
Optical flow estimation is classically marked by the requirement of dense sampling in time. Expand
  • 1,150
  • 166
  • Open Access
Object Segmentation by Long Term Analysis of Point Trajectories
In this paper, we present a method that uses long term point trajectories based on dense optical flow. Expand
  • 709
  • 133
  • Open Access
Segmentation of Moving Objects by Long Term Video Analysis
Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate that motion will be exploited most effectively, if it is regarded over larger time windows. Opposed toExpand
  • 382
  • 92
  • Open Access