Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence.
Ranked #1 on Video Semantic Segmentation on Cityscapes val
Our proposed approach formulates the upsampling task as a sparse problem and employs the normalized convolutional neural networks to solve it.
Existing video stabilization methods either require aggressive cropping of frame boundaries or generate distortion artifacts on the stabilized frames.
To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original video and make the generated pixels match the estimated optical flow.
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
End-to-end training is made possible by differentiable registration and 3D triangulation modules.
The assignment heuristic relies on four metrics: An embedding vector that describes the appearance of objects and can be used to re-identify them, a displacement vector that describes the object movement between two consecutive video frames, the Mahalanobis distance between the Kalman filter states and the new detections, and a class distance.
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
In this paper, we investigate the complimentary roles of spatial and temporal information and propose a novel dynamic spatiotemporal network (DS-Net) for more effective fusion of spatiotemporal information.