Convolutional neural networks (CNNs) have recently been very successful in a variety of
computer vision tasks, especially on those linked to recognition. Optical flow estimation has …

It has been recently shown that a convolutional neural network can learn optical flow
estimation with unsuper-vised learning. However, the performance of the unsuper-vised …

Convolutional neural networks (CNNs) have recently been very successful in a variety of
computer vision tasks, especially on those linked to recognition. Optical flow estimation has …

Optical flow computation is a key component in many computer vision systems designed for
tasks such as action detection or activity recognition. However, despite several major …

Most recent works in optical flow extraction focus on the accuracy and neglect the time
complexity. However, in real-life visual applications, such as tracking, activity detection and …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

Occlusions play an important role in optical flow and disparity estimation, since matching
costs are not available in occluded areas and occlusions indicate motion boundaries …

Learning optical flow with neural networks is hampered by the need for obtaining training
data with associated ground truth. Unsupervised learning is a promising direction, yet the …

We introduce a novel matching algorithm, called DeepMatching, to compute dense
correspondences between images. DeepMatching relies on a hierarchical, multi-layer …

We propose a novel approach for optical flow estimation, targeted at large displacements
with significant occlusions. It consists of two steps: i) dense matching by edge-preserving …