2.6k citations · 3.1k across the 8 of their papers we have counts for
8 papers
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia +3
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…
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
Evgeny Levinkov, Jonas Uhrig, Siyu Tang +7
We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical a…
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation
Benjamin Drayer, Thomas Brox
We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extract…
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Margret Keuper, Siyu Tang, Yu Zhongjie +3
Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks b…
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox +1
Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small…
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg +2
Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training…