The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

We present an unsupervised learning framework for simultaneously training single-view
depth prediction and optical flow estimation models using unlabeled video sequences …

Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames
by watching unlabeled videos via deep convolutional network has made significant progress …

In this work we address task interference in universal networks by considering that a network
is trained on multiple tasks, but performs one task at a time, an approach we refer to as" …

Deep learning techniques hold promise to develop dense topography reconstruction and
pose estimation methods for endoscopic videos. However, currently available datasets do …

In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo
depth estimation using convolutional neural network (CNN) by taking advantages of their …

Optical flow estimation captures the motion information of objects in a scene through
analyzing the displacement of pixels in an image over time. This technology provides a …

Simulators can efficiently generate large amounts of labeled synthetic data with perfect
supervision for hard-to-label tasks like semantic segmentation. However, they introduce a …

Unsupervised deep learning for optical flow computation has achieved promising results.
Most existing deep-net based methods rely on image brightness consistency and local …

Recently end-to-end unsupervised deep learning methods have demonstrated an
impressive performance for visual depth and ego-motion estimation tasks. These data …