Search Results for author:
Found 3 papers, 2 papers with code
Segmenting the motion components of a video: A long-term unsupervised model
Human beings have the ability to continuously analyze a video and immediately extract the motion components.
Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions
In this paper, we propose an original unsupervised spatio-temporal framework for motion segmentation from optical flow that fully investigates the temporal dimension of the problem.
EM-driven unsupervised learning for efficient motion segmentation
The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation.
Ranked #6 on
Unsupervised Object Segmentation
on DAVIS 2016
